Method and apparatus for detecting seizures

ABSTRACT

A method and apparatus for detecting seizures with motor manifestations including detecting EMG signals, isolating from the EMG signals spectral data for a plurality of frequency bands, and calculating a T-squared value there from. The T-squared values may be detected in real time, such as in a patient&#39;s home environment, and the T-squared data may be compared to a threshold T-squared value to determine whether an alarm is sent.

PRIORITY DATA

This application claims the benefit of U.S. Provisional Application No.61/504,582, filed Jul. 5, 2011. The disclosure of U.S. ProvisionalApplication No. 61/504,582 is herein wholly incorporated by reference.

BACKGROUND

A seizure may be characterized as abnormal or excessive synchronousactivity in the brain. At the beginning of a seizure, neurons in thebrain may begin to fire at a particular location. As the seizureprogresses, this firing of neurons may spread across the brain, and insome cases, many areas of the brain may become engulfed in thisactivity. Seizure activity in the brain may cause the brain to sendelectrical signals through the peripheral nervous system to differentmuscles. For example, an electrical signal may originate in the centralnervous system and initiate the propagation of an electrical signalthrough motor neurons. A motor neuron may, for example, communicate witha muscle through interaction with the motor end plate of a muscle fiber;thereby initiating an action potential and depolarization of musclecells within a given motor unit. Depolarization typically results fromthe coordinated flow of ions, e.g., sodium and potassium cations,through channels within a muscle cell membrane. That is, changes instates of ion channels initiate a change in the permeability of a cellmembrane, and subsequent redistribution of charged ions. Current flowthrough muscle cells may initiate a corresponding flow in the tissueabove the muscle and thus an electrical signature at the surface of theskin.

Techniques designed for studying and monitoring seizures have typicallyrelied upon electroencephalography (EEG), which characterizes electricalsignals using electrodes attached to the scalp or head region of aseizure prone individual, or seizure patient. EEG electrodes may bepositioned so as to measure such activity, that is, electrical activityoriginating from neuronal tissue. Compared to EEG, electromyography(EMG) is a little-used technique in which an electrode may be placed onor near the skin, over a muscle, to detect an electrical current orchange in electric potential in response to redistribution of ionswithin muscle fibers.

Detecting an epileptic seizure using electroencephalography (EEG)typically requires attaching many electrodes and associated wires to thehead and using amplifiers to monitor brainwave activity. The multipleEEG electrodes may be very cumbersome and generally require sometechnical expertise to apply and monitor. Furthermore, confirming aseizure requires observation in an environment provided with videocameras and video recording equipment. Unless used in a staffed clinicalenvironment, such equipment is frequently not intended to determine if aseizure is in progress but rather provides a historical record of theseizure after the incident. Such equipment is usually meant forhospital-like environments where a video camera recording or caregiver'sobservation may provide corroboration of the seizure, and is typicallyused as part of a more intensive care regimen such as a hospital stayfor patients who experience multiple seizures. A hospital stay may berequired for diagnostic purposes or to stabilize a patient untilsuitable medication can be administered. Upon discharge from thehospital, a patient may be sent home with little further monitoring.However, at any time after being sent home the person may experienceanother seizure, perhaps fatal.

A patient should in some cases be monitored at home for some length oftime in case another seizure should occur. Seizures with motormanifestations may have patterns of muscle activity that includerhythmic contractions of some, most, or all of the muscles of the body.A seizure could, for example, result in Sudden Unexplained Death inEpilepsy (SUDEP). The underlying causes of SUDEP are not wellunderstood; however, some possible mechanisms causing SUDEP may includetonic activation of the diaphragm muscle so as to prevent breathing,neurogenic pulmonary edema, asystole, and other cardiac dysrhythmia. Ifa sleeping person experiences a seizure involving those conditions, thencaregivers may not be aware that the seizure is occurring, and thus beunable to render timely aid.

While there presently exist ambulatory devices for diagnosis ofseizures, they are EEG-based and are generally not designed or suitablefor long-term home use or daily wearability. Other seizure alertingsystems may operate by detecting motion of the body, usually theextremities. Such systems may generally operate on the assumption thatwhile suffering a seizure, a person will move erratically and violently.For example, accelerometers may be used to detect violent extremitymovements. However, depending upon the type of seizure, this assumptionmay or may not be true. Electrical signals sent from the brain duringthe seizure are frequently transmitted to many muscles simultaneously,which may result in muscles fighting each other and effectivelycanceling out violent movement. In other words, the muscles may work tomake the person rigid rather than cause actual violent movement. Thus,the seizure may not be consistently detected with accelerometer-baseddetectors.

Accordingly, there is a need for an epileptic seizure method andapparatus that can be used in a non-institutional or institutionalenvironment without many of the cumbersome electrodes to the head orextremities. Such an apparatus may be minimally intrusive, minimallyinterfere with daily activities and be comfortably used while sleeping.There is also a need for an epileptic seizure method and apparatus thataccurately detects a seizure with motor manifestations and may alert oneor more local and/or remote sites of the presence of a seizure.Furthermore, there is a need for an epileptic detection seizure methodand apparatus that may be used in a home setting and which may providerobust seizure detection, even in the absence of violent motion, andwhich may be personalizable, e.g., capable of being tailored for anindividual or specific population demographic.

SUMMARY

A method of detecting seizures with motor manifestations including thesteps of detecting EMG signals for a time period, the time periodcomprising a reference period and a monitoring period; using digitalfiltering to isolate from the EMG signals spectral data for a pluralityof frequencies bands selected from the range of about 2 Hz to about 1000Hz; calculating a first T-squared value, the first T-squared value beingdetermined from spectral data for the plurality of frequency ranges,from at least one part of the reference period; calculating a secondT-squared value, the second T-squared value being determined fromspectral data for the plurality of frequency ranges, from at least onepart of the monitoring period; comparing the second T-squared value tothe first T-squared value; and determining whether to trigger an alarmcondition using said comparison of the first T-squared value to thesecond T-squared value.

A method of detecting seizures with motor manifestations including thesteps of detecting EMG signals for a time period, the time periodcomprising a reference period and a monitoring period; using digitalfiltering to isolate from the EMG signals spectral data for a pluralityof frequencies bands selected from the range of about 2 Hz to about 1000Hz; calculating a first PCA value, the first PCA value being determinedfrom spectral data for the plurality of frequency ranges from at leastone part of the reference period; calculating a second PCA value, thesecond PCA value being determined from spectral data for the pluralityof frequency ranges from at least one part of the monitoring period;comparing the second PCA value to the first PA value; and determiningwhether to trigger an alarm condition using said comparison of the firstPCA value to the second PCA value.

A method of detecting seizures with motor manifestations comprisingincluding detecting EMG signals; using digital filtering to isolate fromsaid EMG signals spectral data for a plurality of frequency bandsselected from the range of about 2 Hz to about 1000 Hz; calculating afirst T-squared value from the spectral data; comparing said firstT-squared value to a threshold T-squared value; and determining whetherto trigger an alarm condition using said comparison of the firstT-squared value to the threshold T-squared value.

An apparatus for detecting seizures with motor manifestations, theapparatus comprising one or more EMG electrodes capable of providing anEMG signal substantially representing seizure-related muscle activity;and a processor configured to receive the EMG signal, process the EMGsignal to determine whether a seizure may be occurring, and generate analert if a seizure is determined to be occurring based on the EMGsignal; wherein the processor is capable of processing the EMG signal todetermine whether a seizure may be occurring by calculating a T-squaredstatistical value or a PCA statistical value.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates one embodiment of a seizure detection system.

FIG. 2 illustrates one embodiment of a detection unit and base stationfor a seizure detection system.

FIG. 3 illustrates one embodiment of a base station.

FIG. 4 illustrates one embodiment of a method for monitoring a patientfor seizure activity.

FIG. 5 illustrates one embodiment of a method of adjusting the state ofa detection unit in a method of seizure monitoring.

FIG. 6 illustrates a graphical interface that may be used for accessingdata files.

FIGS. 7A-D illustrate one embodiments of a user interface that may let auser to select and view EMG data.

FIG. 8 illustrates one embodiments of a user interface associated withselecting a file.

FIGS. 9A-D illustrate one embodiment of a user interface that may let auser to adjust various settings.

FIG. 10A-D illustrate one embodiment of a user interface that may let auser to view patient related information.

FIG. 11 illustrates one embodiment of a user interface that allows auser to access and review information associated with a patient.

FIG. 12 illustrates one an EMG trace for a patient showing a monitoringperiod that includes a training region and an episode region.

FIG. 13 illustrates a log periodogram of the training region for the EMGtrace illustrated in FIG. 12.

FIG. 14 illustrates a log periodogram of the episode region for the EMGtrace illustrated in FIG. 12.

FIG. 15 illustrates the EMG waveform of FIG. 12 filtered to pass afrequency range of about 300-400 Hz.

FIG. 16 illustrates the EMG waveform of FIG. 12 filtered to pass afrequency range of about 130-240 Hz.

FIG. 17 illustrates the EMG waveform of FIG. 12 filtered to pass afrequency range of about 30-40 Hz.

FIG. 18 illustrates the EMG waveform of FIG. 12 filtered to pass afrequency range of about 10-50 Hz.

FIG. 19 illustrates the EMG waveform of FIG. 12 filtered to pass afrequency range of about 4-5 Hz.

FIG. 20 illustrates the EMG waveform of FIG. 12 filtered to pass afrequency range of about 3-4 Hz.

FIG. 21 illustrates the EMG waveform of FIG. 12 filtered to pass afrequency range of about 2-3 Hz.

FIG. 22 illustrates the EMG the waveform of FIG. 12 after T-squaredstatistics has been calculated across various frequency ranges.

FIG. 23 illustrates the alarm state for the EMG waveform of FIG. 12.

DETAILED DESCRIPTION

In some embodiments, an apparatus for detecting seizures with motormanifestations may comprise one or more EMG electrodes capable ofproviding an EMG signal substantially representing seizure-relatedmuscle activity, and a processor configured to receive the EMG signal,process the EMG signal to determine whether a seizure may be occurring,and generate an alert if a seizure is determined to be occurring basedon the EMG signal.

In some embodiments, an apparatus for detecting seizures with motormanifestations may comprise a detection unit which includes EMGelectrodes and a base unit in communication and physically separatedfrom said detection unit, wherein the base station is configured forreceiving and processing EMG signals from the detection unit,determining from the processed EMG signals whether a seizure may haveoccurred, and sending an alert to at least one caregiver. In someembodiments, the base station may separately process the data providedby the detection unit for verification of the alarm condition. If thebase station agrees with the alarm, then the base station may generatean alarm to remote devices and local sound generators. Having the basestation agree to the detection unit's alarm introduces a voting concept.Both devices may vote on the decision and agree to sound the alarm. Thismay be used to limit false alarms.

In some embodiments, EMG signals may be collected for a time period andprocessed by filtering to select a plurality of frequency bands. Forexample, an EMG frequency spectrum may be broken up into a number offrequency bands, such as three or more, and one or more characteristicsof each frequency band, for example, power content of the band orspectral density at one or more frequencies within the band, may bemeasured. The “power content” or “power amplitude” of the band mayrelate to muscle work that may be achieved over a given period of timeand may be related to the spectral density (e.g., power per frequency)integrated over a given frequency range. In general, the power contentor spectral density may be measured as a value that is proportional tothe actual power content or spectral density, e.g., when adjusted bysome factor. A measured characteristic for a frequency band may benormalized by its variance and covariance with respect to thecharacteristic as measured in other frequency bands and the resultingnormalized values processed to determine Hotelling's T-squaredstatistics. Based on the T-squared statistical analysis the EMG signalsmay be used to assess whether a seizure incident is declared and whetheran alarm is sent to one or more locations.

In some embodiments, calculation of a T-squared statistical value mayinclude an assignment of weighting factors to the signals from differentfrequency bands. Weighting factors may, for example, be determined byanalyzing signals associated with the various frequency bands obtainedduring a training period. The weight assigned to each of the frequencybands may, for example, be calculated from the inverse of thevariance/covariance matrix of the frequency band calculated during thetraining period. In this approach, a frequency band exhibiting moremuscle activity (usually a lower frequency range) may be assigned lessweight than a frequency band exhibiting lesser muscle activity (usuallya higher frequency range). Weighting factors may also be assigned suchthat signals from a frequency band that exhibits large muscle activityduring normal activity is lessened such as to minimize the probabilitythat a false alarm is initiated and/or to improve discrimination betweennormal activity and seizure episodes. Appropriate weighting may dependupon an individual's normal muscle movement observed during a trainingperiod. For some individuals, the T-squared calculation may essentiallyassign equal weight to the signals from the three frequency bands andfor other individuals the weighting factors may be different, such as toincrease the sensitivity of the technique for detection of seizureactivity.

In some embodiments, multiple frequency bands may be analyzed usingPrincipal Components Analysis (“PCA”). For example, three frequencybands may be selected for each EMG signal, and up to that many, such asthree, principal components may be used. The width of these bands mayvary, with some being narrower and others wider. In some embodiments,statistical parameters based on PCA analysis may be compared to areference value, such as a baseline PCA value determined during atraining period.

In some embodiments, EMG output may be compared to general seizurecharacteristics and to one or more threshold values. If one or morevalues of the EMG output data exceeds one or more thresholds an eventmay be logged, such as by logging the event in a register. For example,EMG output may be used to calculate a T-squared statistical value whichmay be compared to a T-squared threshold value and used to log detectedevents in a register. By way of further example, events logged in one ormore registers may also correspond to the presence of a characteristicGTC waveform, the presence of data bursts, or to other characteristicssuch as further described in U.S. application Ser. No. 13/275,309.Analysis of events logged in registers for various characteristics ofthe output data may be used to assess whether a seizure incident isdeclared and whether an alarm may be sent to one or more locations.

In some embodiments, methods and/or apparatuses described herein may becustomized for an individual or for a patient demographic. For example,in some embodiments, an apparatus may establish or adjust sensitivitysettings based upon a Maximum Voluntary Contraction (MVC) of muscles foran individual. MVC is related to the maximum force a patient may applyduring a voluntary contraction. The strength of muscles may vary fromindividual to individual and the amplitude of the EMG signal may alsovary. Measurement of electromyographic data for a patient during maximumvoluntary muscle exertion (and adjusting sensitivity settingsaccordingly) may customize the technique to an individual's musculatureand may enhance the selectivity of methods described herein fordiscrimination of seizure activity versus data from a non seizureperiod. In some embodiments, a T-squared threshold value may be setbased upon a T-squared statistical value calculated from EMG dataobtained while the individual is at rest and while an individual isundergoing the MVC. In some embodiments, a threshold T-squaredstatistical value may be a value that is greater that the T-squaredstatistical value at rest by some factor of the difference betweenT-squared statistical values calculated during MVC and while theindividual is at rest. In some embodiments, the threshold Z-factor maybe scaled based upon eletromyographic data obtained while the patient isexecuting the MVC.

In some embodiments, seizure detection methods and/or apparatuses asdescribed herein may be adaptive. For example, during or after a seizureevent, a human operator may review and adjust the settings of variousfactors (those factors further described herein), such as the thresholdZ-factor, alarm lag factor, or weighting factors (for signal from aplurality of selected frequency bands), based upon the severity of theseizure, the non-detection of an actual seizure, or a false detection.In some embodiments, detection settings may change automatically, suchas based on the presence of any number of false positive events. Forexample, if an alarm is triggered an individual may be given the optionto cancel the alarm. The system may automatically categorize the eventas a false positive and may be configured to adjust a detection factor,such as, for example, the threshold Z-factor and/or alarm lag, tominimize future false positive event characterizations. As seizure datais collected from one or more patients the settings used to monitor agiven patient or patient demographic may change to better predict futureseizures. For example, detection algorithms may include or use anadjustable template file that comprises various settings and which maybe updated based on the success of the method. The association betweencollected data and seizure related incidents, e.g., declared events,actual seizures and inaccurately reported incidents, may be tracked toevaluate the methods success.

In some embodiments, a method and apparatus may be used, for example, toinitiate an alarm protocol, create a log of seizure incidents to helpmedically or surgically manage the patient, activate a Vagal NerveStimulator, or activate other stimulating devices that may be used toabort or attenuate a seizure. In some embodiments, a log of seizurerelated incidents may prompt a physician to understand more quickly thefailure of a treatment regimen.

The apparatuses and methods described herein may be used to detectseizures and timely alert caregivers of a seizure using EMG, among otherthings. The apparatuses and method may be used, for example, to initiatean alarm protocol, create a log of seizure incidents to help medicallyor surgically manage the patient, activate a Vagal Nerve Stimulator, oractivate other stimulating devices that may be used to abort orattenuate a seizure. In some embodiments, a log of seizure relatedincidents may prompt a physician to understand more quickly the failureof a treatment regimen. The apparatuses and methods may comprise aprocess or processes and device and/or system of devices for detectingseizures with motor manifestations including, but not limited toTonic-Clonic, Tonic-only, or Clonic-only seizures. A “motormanifestation” may in some embodiments generally refer to muscleactivity, whether sustained or otherwise. A motor manifestation may ormay not result in overt movement of an individual's body.

A variety of systems may be suitably used for collecting large amountsof EMG and other patient-related data, prioritizing data for storage,organizing such data for system optimization, and/or initiating an alarmin response to a suspected seizure. FIG. 1 illustrates an exemplaryembodiment of such a system. In the embodiment of FIG. 1, a seizuredetection system 10 may include a detection unit 12, an optional basestation 14, an optional video camera 9 and an optional alert transceiver16. The detection unit may comprise one or more EMG electrodes capableof detecting electrical signals from muscles at or near the skin surfaceof a patient, and delivering those electrical EMG signals to a processorfor processing. The base station may comprise a computer capable ofreceiving and processing EMG signals from the detection unit,determining from the processed EMG signals whether a seizure may haveoccurred, and sending an alert to a caregiver. An alert transceiver maybe carried by, or placed near, a caregiver to receive and relay alertstransmitted by the base station.

In using the apparatus of FIG. 1, a person 11 susceptible to epilepticseizures may be resting in bed or may be at some other location as dailyliving may include, and may have a detection unit 12 in physical contactwith or in proximity to his or her body. The detection unit 12 may be awireless device so that a person may be able to get up, walk around, andengage in daily activities without having to be tethered to an immobilepower source or to a bulkier base station 14. For example, the detectionunit 12 may be woven into a shirt sleeve, or may be mounted to anarmband or bracelet. In other embodiments, one or more detection units12 may be placed or built into a bed, a chair, an infant car seat, orother suitable clothing, furniture, equipment and accessories used bythose susceptible to seizures. The detection unit 12 may comprise asimple sensor, such as an electrode, that may send signals to the basestation for processing and analysis, or may comprise a “smart” sensorhaving some data processing and storage capability. In some embodiments,a simple sensor may be connected via wire or wirelessly to abattery-operated transceiver mounted on a belt worn by the person.

The system may monitor the patient, for example, while resting, such asduring the evening and nighttime hours or while engaged in some otheractivity. If the detection unit 12 on the patient detects a seizure, thedetection unit 12 may communicate wire or wirelessly, e.g., via acommunications network or wireless link, with the base station 14, to aremote cell phone or other hand held device via bluetooth orsimultaneously to a base station and remote cell phone. The detectionunit 12 may send some signals to the base station device for morethorough analysis. For example, the detection unit 12 may process anduse EMG signals (and optionally ECG and temperature sensor signals) tomake an initial assessment regarding the likelihood of occurrence of aseizure, and may send those signals and its assessment to the basestation 14 for separate processing and confirmation. If the base station14 confirms that a seizure is likely occurring, then the base station 14may initiate an alarm for transmission over the network 15 to alert acaregiver by way of email, text, or any suitable wired or wirelessmessaging indicator. In some embodiments, if one or more of thedetection unit 12, the base station 14, or a caregiver, e.g., a remotelylocated caregiver monitoring signals provided from the base station,determines that a seizure may be occurring a video camera 9 may betriggered to collect information. In some embodiments, a cell phone orother hand held device may be configured with an application thatenables more thorough analysis than the analysis performed by thedetection unit 12. Thus, a cell phone may, e.g., serve some of the sameor similar functions as a base station.

The base station 14, which may be powered by a typical household powersupply and contain a battery for backup, may have more processing,transmission and analysis power available for its operation than thedetection unit 12, may be able to store a greater quantity of signalhistory, and evaluate a received signal against that greater amount ofdata. The base station 14 may communicate with an alert transceiver 16located remotely from the base station 14, such as in the bedroom of afamily member, or to a wireless device 17, 18 carried by a caregiver orlocated at a work office or clinic. The base station 14 and/ortransceiver 16 may send alerts or messages to caregivers, or medicalpersonnel via any suitable means, such as through a network 15 to a cellphone 17, PDA 18 or other client device. The system 10 may thus providean accurate log of seizures, which may allow a patient's physician tounderstand more quickly the success or failure of a treatment regimen.Of course, the base station 14 may simply comprise a computer havinginstalled a program capable of receiving, processing and analyzingsignals as described herein, and capable of transmitting an alert. Inother embodiments, the system 10 may simply comprise, for example, EMGelectrodes and a smartphone, such as an iPhone, configured to receiveEMG signals from the electrodes for processing the EMG signals asdescribed herein using an installed program application. In furtherembodiments, so-called “cloud” computing and storage may be used vianetwork 15 for storing and processing the EMG signals and related data.In yet other embodiments, one or more EMG electrodes could be packagedtogether as a single unit with a processor capable of processing EMGsignals as disclosed herein and sending an alert over a network. Inother words, the apparatus may comprise a single item of manufacturethat may be placed on a patient and that does not require a base stationseparate transceiver. In some embodiments, an alarm may cause the cellphone to dial out to a predetermined phone number with, e.g., a testmessage and may be used to open a voice comm link.

In the embodiment of FIG. 1, the signal data may be sent to a remotedatabase 19 for storage. In some embodiments, signal data may be sentfrom a plurality of epileptic patients to a central database 19 and“anonymized” to provide a basis for establishing and refininggeneralized “baseline” sensitivity levels and signal characteristics ofan epileptic seizure. The database 19 and base station 14 may beremotely accessed via network 15 by a remote computer 13 to allowupdating of detector unit and/or base station software, and datatransmission. The base station 14 may generate an audible alarm, as maya remote transceiver 16. All wireless links may be two-way for softwareand data transmission and message delivery confirmation. The basestation 14 may also employ one or all of the messaging methods listedabove for seizure notification. The base station 14 may provide an“alert cancel” button to terminate the incident warning.

In some embodiments, a transceiver may additionally be mounted within aunit of furniture or some other structure, e.g., an environmental unitor object. If a detection unit is sufficiently close to thattransceiver, such a transceiver may be capable of sending data to a basestation. Thus, the base station may be aware that information is beingreceived from that transceiver, and therefore the associatedenvironmental unit. In some embodiments, a base station may select aspecific template file or use associated device settings, including, forexample, threshold Z-factor, weighting coefficients, alarm lag settings,and/or other settings as described further herein, in a manner that maybe dependent upon whether or not it is receiving a signal from a certaintransceiver. Thus, for example, if the base station receives informationfrom a detector and from a transceiver that is associated with a bed orcrib it may treat the data differently than if the data is received froma transceiver associated with another environmental unit, such as, forexample, a unit attached to a restroom sink or clothing typically wornwhile an individual may be exercising. Moreover, the detection unit andbase station may have input output capability and, in some embodiments,adjustment of settings on one unit may result in correspondingadjustment of setting in the other.

The embodiment of FIG. 1 may be configured to be minimally intrusive touse while sleeping or minimally interfere in daily activities, mayrequire a minimum of electrodes such as one or two, may require noelectrodes to the head, may detect a seizure with motor manifestations,may alert one or more local and/or remote sites of the presence of aseizure, and may be inexpensive enough for home use.

FIG. 2 illustrates an embodiment of a detection unit 12 or detector. Thedetection unit 12 may include EMG electrodes 20, and may also includeECG electrodes 21. The detection unit 12 may further include amplifierswith leads-off detectors 22. In some embodiments, one or more leads-offdetectors may provide signals that indicate whether the electrodes arein physical contact with the person's body, or otherwise too far fromthe person's body to detect muscle activity, temperature, brain activityor other patient phenomena.

The detection unit 12 may further include a temperature sensor 23 tosense the person's temperature. Other sensors (not shown) may beincluded in the detection unit, as well, such as accelerometers. Signalsfrom electrodes 20 and 21, temperature sensor 23 and other sensors maybe provided to a multiplexor 24. The multiplexor 24 may be part of thedetection unit 12 or may be part of the base station 14 if the detectionunit 12 is not a smart sensor. The signals may then be communicated fromthe multiplexor 24 to one or more analog-to-digital converters 25. Theanalog-to-digital converters may be part of the detection unit 12 or maybe part of the base station 14. The signals may then be communicated toone or more microprocessors 26 for processing and analysis as disclosedherein. The microprocessors 26 may be part of the detection unit 12 ormay be part of the base station 14. The detection unit 12 and/or basestation 14 may further include memory of suitable capacity. Themicroprocessor 26 may communicate signal data and other informationusing a transceiver 27. Communication by and among the components of thedetection unit 12 and/or base station 14 may be via wired or wirelesscommunication. In some embodiments, the detection unit 12 may beequipped with audio recording capability and may be configured forsending an audio signal in addition to other transmitted data.

Of course, the exemplary detection unit of FIG. 2 may be configureddifferently. Many of the components of the detector of FIG. 2 may bebase station 14 rather than in the detection unit 12. For example, thedetection unit may simply comprise an EMG electrode 20 in wirelesscommunication with a base station 14. In such an embodiment, A-Dconversion and signal processing may occur at the base station 14. If anECG electrode 21 is included, then multiplexing may also occur at thebase station 14.

In another example, the detection unit 12 of FIG. 2 may comprise anelectrode portion having one or more of the EMG electrode 20, ECGelectrode 21 and temperature sensor 23, in wired or wirelesscommunication with a small belt-worn transceiver portion. Thetransceiver portion may include a multiplexor 24, an A-D converter 25,microprocessor 26, transceiver 27 and other components, such as memoryand I/O devices (e.g., alarm cancel buttons and visual display).

FIG. 3 illustrates an embodiment of a base station 14 that may includeone or more microprocessors 30, a power source 31, a backup power source32, one or more I/O devices 33, and various communications means, suchas an Ethernet connection 34 and transceiver 35. The base station 14 mayhave more processing and storage capability than the detection unit 12,and may include a larger electronic display for displaying EMG signalgraphs for a caregiver to review EMG signals in real-time as they arereceived from the detection unit 12 or historical EMG signals frommemory. The base station 14 may process EMG signals and other datareceived from the detection unit 12. If the base station 14 determinesthat a seizure is likely occurring, it may send an alert to a caregivervia transceiver 35. In some embodiments, the base station 14 may beequipped to receive a signal including audio data (such as from adetection unit 12), may be equipped audio recording capability or both.The base station may also be configured for sending audio data to acaregiver in addition to other transmitted data.

Various devices in the apparatus of FIGS. 1-3 may communicate with eachother via wired or wireless communication. The system 10 may comprise aclient-server or other architecture, and may allow communication vianetwork 15. Of course, the system 10 may comprise more than one serverand/or client. In other embodiments, the system 10 may comprise othertypes of network architecture, such as a peer-to-peer architecture, orany combination or hybrid thereof.

A normal voluntary muscle movement may comprise a coordinatedcontraction of agonist and antagonistic muscles in a cooperative way toachieve a particular motion. Often during seizures, such coordination islost. Instead, the muscles may tend to lock up different parts of thebody with muscles fighting each other. A good example of this scenariomay be seen in the tonic phase of a motor seizure when the biceps andtriceps muscles are both stimulated. These muscles may fight each otherwith very high amplitude signals, but the arms may not move very much atall. This higher frequency electrical activity may, for example, becharacteristic of a Generalized Tonic-Clonic (GTC) seizure orgeneralized tonic seizure. Thus, motor manifestations (andhigh-amplitude EMG signals) may be present without overt signs ofmovement. As noted previously, seizures that exhibit suchcharacteristics may not be consistently detected withaccelerometer-based detectors. The EMG electrodes described herein maydetect the motor manifestations of a patient (even where overt signs ofmuscle activity are absent), and may generate EMG signals suitable forprocessing and analysis according to a variety of methods.

EMG electrodes may be placed on or near one or more muscles, such as thebiceps. Alternatively, one EMG detector may be placed over a bicepsmuscle and one EMG detector placed over a triceps muscle on the same armusing an elastic arm band, clothing, tape or some other method ofmaintaining the electrodes in contact over those muscles. If the one ormore muscles that are monitored include an agonist and antagonist musclepair, the relation of EMG data between the muscles and whether such datamay be correlated as might be expected in coordinated movement, may beused to distinguish normal muscle behavior from motor manifestationsassociated with a seizure. As a seizure begins, the amplitude of thesignals from the EMG electrodes may begin to increase until theamplitude is quite high. However, high amplitude signals may notnecessarily characterize a real seizure. Many body movements, includingnight terrors, may also result in high amplitude signals. Therefore,merely detecting high amplitude signals may not result in seizuredetection. High amplitude signals sustained for a somewhat longer periodof time may provide a better indication of a seizure.

To better characterize normal and seizure-related muscle activitymonitoring as described herein may involve not only observing changes inEMG signal amplitude, techniques herein may further evaluate thevariance/covariance of power amplitudes for selected frequency bands,and use such data to calculate a T-squared statistical value. Observingchanges in signals from the selected spectral bands (and, for example,normalization of signals from the spectral bands based onvariance/covariance matrices—such as using an inverse matrix determinedthere from) may assist in differentiating between actual seizures andevents such as night terrors that, if monitored, otherwise may bechallenging to tell apart. The magnitude of T-squared statistical valuesmay, e.g., reflect a condition wherein the distribution of signalsacross different frequency bands has changed. For example, when certainhigh frequency signals grow in relative amplitude to other frequencyranges a calculated T-squared statistical value may change in a highlysensitive manner. Moreover, in some situations, T-squared values maychange even if the total power has not significantly changed.

A number of noise sources such as 1/f and some environmental sources mayincrease the overall power and overall positive amplitude of EMG databut may be readily distinguished from EMG signal originating from aseizure using the techniques described herein. For example, some sourcesof noise comprise signals from different frequency bands that may changein characteristic ways and the system may be programmed or adapted todistinguish such changes from those associated with an actual seizure.Moreover, during a seizure, high frequency components may show largevariance/covariance with other regions of an EMG spectrum. Measurementof variances/covariances (and T-squared statistical values calculatedthereof) and/or adjustment of weights associated with bands maytherefore serve as a tool to augment differences between EMG signalsoriginating from various sources, including, e.g., a seizure, normalmuscle movement and various sources of noise (such as in particularthose that are frequency dependent), thereby facilitatingcharacterization and detection of such events.

In the normal recruitment of muscle activity in voluntary contractions,low frequency signals (about 30 Hz) may be sent to recruit muscles, andlater, as the activity becomes more intense, higher frequency signalsmay be sent to maintain the muscle contraction. Such signals may be ashigh as 300 Hz-400 Hz, although signals sent in trained athletes may beup to about 1,000 Hz. The electrical frequencies recorded withmacro-electrodes may frequently extend above 300 Hz in a sustainedmanner during a seizure with motor manifestations. However, above about300 Hz-400 Hz, the signal may generally be considerably weaker. Eventhough high frequency signals may be relatively weak and high frequencysignal amplitudes may contribute a small fraction of the total power,for some individuals, the presence of such high frequency signals and/orchanges in signal ratio between high frequency ranges and otherfrequency ranges may be highly indicative of a seizure. Such changes maybe captured, as described herein, by analysis of variances/covarianceswithin and between frequency bands, information that may be lost withoutpartitioning of frequency data in different bands and use of T-squaredstatistical methods.

In some embodiments, relative changes in signals derived from variousfrequency components for an individual undergoing normal muscle activitymay be tracked and compared to changes in frequency components for thatindividual as that individual experiences a seizure. Weighting factorsassociated with the measured power in various frequency bands may beadjusted to distinguish between typical normal muscle movement, sourcesof noise, and seizure activity. In some embodiments, variablesincluding, e.g., threshold Z-values, lag settings, weighting factorsassociated with one or more frequency band or combinations thereof, maybe adjusted to distinguish between typical normal muscle movement,sources of noise, and seizure activity.

A seizure-prone individual may, in some embodiments, be monitored bycollecting EMG data during a monitoring period, calculation ofstatistical values, and comparison of those values to reference EMG dataor values collected during one or more reference periods. A referenceperiod may include monitoring an individual in a supervised setting,such as during a period with independent verification (such as withvideo monitoring) of any seizures that may occur. A reference period maybe the first time an individual wears a device. However, a referenceperiod may be also be repeated, such as if deemed necessary, or may beperformed at other times, such as after an individual has becomeaccustomed to wearing the device.

During a reference period, an individual may be monitored while engagedin various activities. For example, in some embodiments, a referenceperiod may include collection of EMG data while the individual is atrest, executing common daily activities, executing activities which maytypically involve vigorous and/or repetitive motion (such as, by way ofnonlimiting example, the execution of a maximum voluntary contraction),or any combinations thereof. EMG data collected while executingdifferent activities may be used to characterize resting and/or elevatedportions of muscle activity that may be experienced by a patient andwhich may be distinguished over seizure activity. Such activity may beused to characterize baseline activity that may, in some embodiments, beused to establish a reference T-squared value to which T-squared valuesin a monitoring period are compared, such as by calculation of adifference between those T-squared values. In some embodiments, a methodmay, periodically re-normalize EMG data collected in a monitoring periodby executing either or both of an electrode re-normalization and atraining period (with recalculation of a variance/covariance matrix,such as by determining an inverse matrix, and establishment of a set ofweighting coefficients) as part of reference period(s) that are repeatedduring daily use. Re-normalization may effectively re-center muscleactivity around a T-squared baseline that provides a small T-squaredvalue and this small value may be used as a reference T-squared baselinefrom which T-squared values are compared, such as by subtraction ofthose values or by calculation of a delta value between them. In someembodiments, a reference T-squared value may be rounded to zero and/ordropped from the calculation. Thus, in some embodiments, a T-squaredvalue obtained during monitoring may be used (without subtraction eachtime by a reference value each time that value is calculated). This maybe considered comparing the T-squared value during monitoring to a valueof zero. Periodic sampling of the electrodes during monitoring may, insome embodiments, be used to verify that a reference T-squared valueduring times after a normalization remains low, and that the comparisonof a monitoring T-squared value to a number that is small (or zero) doesnot result in incorrect analysis of muscle activity.

EMG data obtained during a reference period may be used to calculate avariance/covariance matrix and/or inverse matrix for the T-squaredstatistic at a time when a patient is not undergoing a seizure event,and may, in some embodiments, also be used to establish a baseline orreference T-squared statistical value. For example, during a referenceperiod, the apparatus may self-train for an individual by calculatingthe variance/covariance matrix for the T-squared statistic using EMGdata obtained when an individual is initially connected. Calculating theT-squared statistic across selected frequency bands during anon-seizure-resting period may provide a baseline value against whichsubsequent signals are analyzed. Calculating the variance/covariancematrix for the T-squared statistic across selected frequency bandsduring a non-seizure-resting period may be used to establish weightingcoefficients that may be used in subsequent calculations of T-squaredstatistical values. In some embodiments, an inverse matrix may becalculated and weights assigned to each of a plurality of selectedfrequency bands may be determined from the coefficients of the inversionmatrix. The inverse matrix of the variance/covariance matrix may be amatrix (which may also be referred to as a reciprocal matrix) that, whenconvoluted with the variance/covariance matrix, produces the identitymatrix. Use of the inversion matrix to establish weights and normalizeT-squared values has been found to be a way to automatically adjust thecontribution of different frequency bands to a patient's musculature.

In some embodiments, a reference period (or periods) may be used toestablish device settings, such as, e.g., the threshold Z-factor orthreshold T-squared value, weighting factors between frequency regions,alarm lag settings, the variance/covariance matrix for the referenceT-squared statistic, standard deviation of the reference T-squaredstatistic, other factors, or any combinations thereof. Future periods ofuse (monitoring periods) may involve electrode normalization (such asinvolving adjustment of amplifier and/or digital signal gain settings);however, other device settings (such as noted above) may, at least insome embodiments, not be subject to change—the predetermined values ofthose constants (determined from one or more reference periods) may beheld constant and used for the entirety of the monitoring period. Forexample, after determination of device settings in the one or morereference periods, monitoring an individual for seizure activity may, insome embodiments, involve normalization of electrodes, collection of EMGdata with calculation of a T-squared value, comparison of that data witha predetermined threshold T-squared value, and evaluation of whether analarm may be initiated. A predetermined threshold T-squared value may,in some embodiments, involve collection of a T-squared referencebaseline (during a reference period) and setting the threshold T-squaredvalue to be some number of standard deviation units of the collectedT-squared reference baseline. A predetermined threshold T-squared valuemay, in some embodiments, involve collection of a T-squared value(during a reference period while the patient is executing an MVC) andsetting the threshold T-squared value to be a certain factor of theT-squared value obtained while executing the MVC. A predeterminedthreshold T-squared value may, in some embodiments, involve collectionof a T-squared value (during a reference period and while executing anMVC), repeating the calculation of a T-squared value MVC several times,calculating a standard deviation for the several repetitive T-squaredcalculations performed during the MVC, and scaling the thresholdT-squared value to be some number of the calculated standard deviationfrom the repetitive T-squared values obtained during the MVC. In someembodiments, after determination of device settings in the one or morereference periods, monitoring an individual for seizure activity may, insome embodiments, involve normalization of electrodes, collection of EMGdata with calculation of a T-squared value, comparison of that data witha reference T-squared value and evaluation of whether an alarm may beinitiated.

In some embodiments, comparison of T-squared values (e.g., comparison ofa T-squared value determined during a monitoring period and a referenceT-squared value) and evaluation of whether to initiate an alarm mayinvolve scaling the difference between T-squared values in units ofstandard deviations of the reference T-squared statistic and assessmentof whether the number of standard deviations exceeds a thresholdZ-factor (in units of standard deviations) or exceeds that factor for acertain time interval. A threshold T-squared value may exceed areference T-squared value by some value, such as a certain number ofstandard deviations (and may be referred to as a threshold Z-factor) ormay be scaled in some other way—such as using an MVC calculationdescribed above or set to be an acceptable value that has beendetermined to work for all patients of a certain demographic (such aspatients with a certain mid upper arm circumference).

In some embodiments, a reference period or periods may be used toestablish device settings, and monitoring periods may use thosesettings. A monitoring period may involve electrode normalization (suchas involving adjustment of amplifier and/or digital signal gainsettings), but otherwise use device settings predetermined in thereference periods. In some embodiments, a reference period mayadditionally or alternatively be executed during the same period oftime, for example, on the same day or interval, that the system ismonitoring a patient. Such a reference period may be conducted at anindividual's home. For example, in some embodiments, an apparatus may beconfigured to initiate an automatic calibration (such as may involveadjustment of electrode gain settings) and the apparatus may benormalized to account for, e.g., how tightly the EMG sensors are placedover a muscle, humidity, EMG sensor location, or other factors.Normalization may, in some embodiments, include re-training the systemby collection of data for a period of time as necessary to recalculatethe variance/covariance matrix for T-squared statistical calculationsand setting of weighting factors, or both. Normalization may, in someembodiments, include calibration of electrodes (such as includingadjustment of gain settings and/or establishing that a common moderejection between detector inputs is within an acceptable and/orexpected range) and system training with the collection of data forcalculation of a reference T-squared statistical value, the collectionof data for calculation of weighting factors between selected bands, orboth. Upon collection of data during normalization, the system mayestablish a reference T-squared statistical value, may replace a defaultreference T-squared statistical value, or replace a previously usedreference T-squared value. For example, the system may substitute thenewly determined reference T-squared value for one determined. During are-training period, the system may, in some embodiments, also bemonitoring the patient for seizure activity. For example, during thecollection of data for training, the system may compare the data, i.e.,as collected in real-time, to reference values, such as may have beenestablished from a previous training period or patient demographic, andevaluate whether a seizure may be occurring. As a system completesmonitoring within a given time period, the system may incorporate datafrom that time period into settings for evaluation of a subsequentperiod. The system may, in some embodiments, evaluate the data collectedduring normalization and only incorporate the recently collected data(and update settings) if the data meets certain requirements. Forexample, updating a given setting may depend upon whether the value ofthe reference T-squared value, standard deviation of the referenceT-squared baseline or both are within accepted boundaries. The systemmay also execute a routine wherein spurious data, e.g., dataoutliers—such as from random noise or inadvertent rapid movements of theindividual may be removed. Normalization may occur at regular intervalsor in response to a certain criteria. The system may, for example,automatically re-train the system by collecting EMG data from a timeperiod when an individual is initially connected to the device or when asufficient increase or decrease in the average amplitude of a signaloccurs or occurs over a certain time period.

Thus, different embodiments may incorporate settings from either areference period that is conducted in a supervised setting, duringperiodic normalization (such as including periodic normalization in homeuse), or both. In addition, in some embodiments, at least some devicesettings may be established based on data derived from all patients orfrom patients of a certain demographic. For example, as an alternativeto monitoring of an individual during a reference period in a supervisedsetting, an individual may use a device which has been programmed withsettings that are specific for all patients or for patients of a certaindemographic. By way of non-limiting, as an alternative to setting athreshold T-squared value or threshold Z-factor based on MVC or scalingin terms of the standard deviation of a reference period those settingsmay be set based on typical settings that have been shown to work forpatients with a given mid upper arm circumference (MUAC), such as byusing one threshold setting for individuals with MUAC values of about 16cm to about 24 cm and a different setting for individuals with MUACvalues of about 24 cm to about 28 cm. In some embodiments, an individualmay wear a device and, upon initial monitoring, the EMG electrodes mayexecute a calibration routine (such as involving adjustment of amplifierand/or digital signal gain settings); however, other device settings,such as, for example, reference T-squared statistic, threshold Z-factor,and alarm lag settings, may be population specific. In otherembodiments, the reference T-squared statistic may be automaticallydetermined upon initially connecting the device, such as, for example,along with calibration and during training, and therefore may becustomized to the individual. However, other device settings such as thethreshold Z-factor and alarm lag settings may be population specific.Thus, some of the aforementioned approaches may facilitate monitoring ofa patient without the need for a patient to spend time in a supervisedsetting.

In some embodiments, device settings may be adjustable only uponexternal verification and review by a human operator. In otherembodiments, adjustment of device settings may alternatively oradditionally be executed automatically. For example, as previouslynoted, if an alarm is triggered, an individual may be given the optionto cancel the alarm. In such embodiments, the device may be programmedto log such cancellations as false-positive events. The system maycategorize the event as a false positive and the system may beconfigured to adjust a detection factor, such as, for example, thethreshold Z-factor and/or alarm lag, to minimize future false-positiveevent characterizations. For example, the threshold Z-factor may beincreased by an amount, such as about 10 standard deviation units, eachtime a false-positive event is logged. Alternatively, if the system isscaled in terms of the difference in a reference T-squared baseline andT-squared calculation while executing an MVC, the system may adjust athreshold T-squared value to adjust detection sensitivity.

A device may automatically adjust a setting, such as the thresholdZ-factor, each time a false-positive event is logged or the system mayonly adjust detection setting following a certain number offalse-positive event detections. In some embodiments, the system mayalso include in memory (or access when needed—such as while adjustingsetting factors) a record of data from any actual seizures that werecorrectly identified during previous monitoring periods and/or weredetected during a reference period. The system may adjust detectionsettings and calculate whether the new settings would have resulted inthe system missing detection of any previously recorded seizures. Thus,the historical record of detected seizures may serve as a qualifyingcheck to the adjustment of settings. The system may, in someembodiments, adjust settings to find the ideal set point for the variousdetection settings. For example, either a detection unit or a basestation (which, as previously noted, may have more advanced programmingcapability) may execute an algorithm to find settings that provideoptimum selectivity between actual recorded seizures and any events thathad been recorded as false positive events. In adjusting settings, thedetection system may store or have access to recorded seizures as wellas any false-positive events. Moreover, the system (for example,detector unit and/or base station) may store EMG data, that while notsufficient to initiate an alarm, may have been sufficiently close todoing so. The system may then be able to verify that adjustment ofsettings and use of those settings would not have resulted in afalse-positive detection with regard to the stored data. Therefore, thesystem may have access to the relevant data to dynamically adjustsettings to optimize detection of actual seizures and minimizefalse-positive events and may furthermore do so automatically.

FIG. 4 illustrates an exemplary method 36 of monitoring EMG signals forseizure characteristics and initiating an alarm response if a seizure isdetected. In a step 38, EMG signals (and optionally other detectoroutput signals) may be collected, such as in an ambulatory or homesetting. In a step 40, the EMG signals may be processed by filtering toselect a plurality of frequency bands of various widths. Filtering maybe accomplished by software or electronic circuit components, such asbandpass filters (e.g., Baxter-King bandpass filters) suitably weighted.For example, in some embodiments, three frequency bands may be selectedas follows: Range 1 may range from about 300 Hz to about 400 Hz, Range 2may range from about 130 Hz to about 240 Hz, and Range 3 may range fromabout 30 Hz to about 40 Hz. Data from the selected frequency bands maybe processed in a step 42 and one or more statistical values may bedetermined. For example, a T-squared statistical value may be calculatedbased on the variances/covariances of the power amplitudes in theselected frequency bands. In a step 44 reference data may be determined,selected, or used and the one or more statistical values calculated(step 42) may be compared (step 46) to the reference data. In someembodiments, a T-squared value may be compared to a baseline that is asmall number, near zero, or which may be zero because the baseline wasadjusted to be so (such as by adjusting weighting coefficients,electrode normalization, or both) based on reference data collectedduring a reference period. In some embodiments, the difference between aT-squared statistical value and a reference T-squared statistical value(ΔT-squared) may be determined. In a step 48, the method may assesswhether a seizure event may be occurring. Such an assessment may, forexample, involve scaling ΔT-squared by the number of standard deviationsunits in which it differs from a reference T-squared baseline, setting athreshold T-squared value based on an MVC, setting a threshold based ona demographic criterion, or setting a threshold using a combination ofways. A scaled value of ΔT-squared, e.g., the Z-factor (in units ofstandard deviations) may be compared to a threshold Z-factor orthreshold T-squared value to determine the likelihood that a seizure maybe occurring. Based on the determined likelihood that a seizure may beoccurring, the method may initiate an alarm protocol (step 50) and/orthe system may collect a next set of EMG data (step 52) and again assesswhether to initiate an alarm.

As described in step 38, methods herein involve the collection of EMGdata. In some embodiments, detection of seizures may be accomplishedexclusively by analysis of EMG electrode data. In other embodiments, acombination of one or more EMG sensors and one or more other sensors maybe used. For example, temperature sensors, accelerometers, ECG sensors,other sensors, or any combinations thereof, may be used. Accelerometersmay, for example, be placed on a patient's extremities to detect thetype of violent movement that may characterize a seizure. Similarly, ECGsensors may be used to detect raised or abnormal heart rates that maycharacterize a seizure. A monitoring device may detect an epilepticseizure without the customary multitude of wired electrodes attached tothe head, as typical with EEG. Combination of EMG with other data may,for example, be used with particularly difficult patients. Patients withan excessive amount of loose skin or high concentrations of adiposetissue, which may affect the stability of contact between an electrodeand the skin, may present monitoring challenges and may be candidatesfor use with multiple sensors. Data from non-EMG sensors (if present)may be used in a number of ways. For example, in some embodiments, thethreshold Z-factor or time lag factor may be adjusted based on thepresence of data corroborating (or contradicting) that a seizure may beoccurring. Thus, the sensitivity of the system to the EMG data may bemodified based on corroborating or contradicting evidence from othersensors. In some embodiments, the selection of a specific reference filemay, at least in part, be based on data collected by another sensor.

In some embodiments, an EMG detector may comprise a single detectionunit such as placed on the biceps of an individual. In otherembodiments, a combination of two or more detection units may be used.For example, EMG detectors may be attached to an agonist and antagonistmuscle group or signals from other combinations of muscles may becollected. In general, the EMG data collected in step 38 may be from anyof various suitable types of electrodes, such as, for example, surfacemonopolar electrodes, bipolar differential electrodes or electrodes ofanother suitable geometry. Such electrodes may, for example, bypositioned on the surface of the skin, may or may not includeapplication of a gel, and may, in some embodiments, be Ag/AgClelectrodes. The use of a bipolar EMG electrode arrangement, e.g., with areference lead and two surface inputs, allows for the suppression ofnoise that is common to those inputs. For example, a differentialamplifier may be used, and a subtraction of the signals from one inputwith respect to the other may be executed. Differences in input signals,such as originating from depolarization of a muscle group, may thereforebe selectively amplified and signals that are common to both inputs(such as external noise) may be substantially nullified. As previouslynoted, in some embodiments, calibration or normalization of electrodesmay be periodically executed. Electrode normalization may, in someembodiments, include application of a test pulse, typically analternating current, to either or both of the detection or referenceinputs of an EMG detection unit. The output signal from the test pulsemay be used to execute a calibration routine (such as involvingadjustment of amplifier and/or digital signal gain settings).

In a step 40, the EMG signals may be processed by filtering to select aplurality of frequency bands of various widths. Filtering may beachieved using software or electronic circuit components, such asbandpass filters (e.g., Baxter-King filters), suitably weighted. Step 38(collection of data) and step 40 (selection of frequency regions ofinterest) may be described conveniently as distinct steps. However, suchdescription should not be interpreted as limiting methods herein tofiltering with either software or electronic circuit components, forexample, analog or digital signal processing—either techniques and orcombinations of analog and digital signal processing may be used forisolation of spectral data. In some embodiments, three frequency bandsmay be selected including a first range, a second range, and a thirdrange. The first range may range from about 250 Hz to about 420 Hz, orabout 300 Hz to about 400 Hz, or about 325 Hz to about 375 Hz. Thesecond range may range from about 80 Hz to about 290 Hz, or about 130 Hzto about 240 Hz, or about 150 Hz to about 220 Hz. The third range mayrange from about 10 Hz to about 50 Hz, or about 30 Hz to about 40 Hz, orabout 32 Hz to about 38 Hz. In some embodiments, the frequency bandsselected in a step 40 may be based on a measured waveform previouslyanalyzed during a reference period. For example, during a referenceperiod, spectral data from a seizure period may be measured and thatdata may be compared to or fit to a generalized GTC waveform. A GTCwaveform includes a number of characteristic reference points which maybe correlated to spectral data for a given patient. For example, a GTCwaveform may show a region of depressed spectral density (local minimum)near about 280 Hz to about 320 Hz, a region of elevated spectral density(local maximum) near about 380 Hz to about 420 Hz, and an inflectionpoint between local extreme values. Such characteristic reference pointsmay be identifiable in different individuals but for those patients thepoints may be found at different frequencies. The position of thosereference points may be associated with a patient's individualmusculature, and for some individuals may be related to motormanifestations during a seizure. In some embodiments, the position of acharacteristic reference point, including, for example, a local minimumvalue, a local maximum value, or an inflection point, may be used toselect or adjust the position of at least one of a plurality of theplurality of selected bands used in a T-squared calculation. In someembodiments, the number of frequency regions selected for analysis maybe greater than three. For example, some methods, particularly but notlimited to those that use PCA analytical methods may involve theselection of greater than three ranges. Selection of frequency bands maybe made by collecting EMG data for a period of time and converting thedata using Fast-Fourier Transform (FFT) techniques. In some embodiments,data may be collected for an epoch of about one half second to about twoseconds (or for some other period) and converted to the frequencydomain.

In a step 42, EMG data (from the selected frequency regions) may beprocessed using any of various statistical techniques. For example, insome embodiments, data from three selected frequency ranges may be usedto determine Hotelling's T-squared statistics. Hotelling's T-squaredtest assumes that data are independent and multivariate Gaussian. Thoseassumptions are generally invalid for EMG data; however, it has beendiscovered that Hotelling's T-squared statistic may serve as a metricfor recognizing when amplitudes of measured powers in the selectedfrequency ranges have changed, thus possibly signaling a seizureepisode. Accordingly, Hotelling's T-squared statistic may be considereda metric in which the amplitude of measured power for each frequencyrange is normalized by its variance and its covariance with the poweramplitudes measured in other frequency ranges. This approach may producea more sensitive and stable indicator of a seizure event or alarmcondition than using the power in a single frequency range or using thetotal power for a combination of frequency ranges without convolution ofthe data by normalization of the data by its variance/covariance withother selected ranges. Importantly, because the system is more sensitiveto seizure activity than, e.g., measurement of integrated power contentover one or more frequency ranges, threshold values for detection may beincreased (thus avoiding many false-positive detections) without missingthe detection of a seizure. In some embodiments, a T-squared analysismay be executed upon the power amplitude within selected bands. In otherembodiments, T-squared analysis may use other characteristics of the EMGdata—alternatively, or in combination with power amplitudes. Forexample, the spectral density at one or more discrete frequencies in agiven band, e.g., the peak spectral density in a certain band, or themean spectral density within a band may also be processed withHotelling's T-squared statistics. In addition, data from other sensorsthat may be present, such as, for example, temperature, accelerometer,or ECG data, may also be processed.

The T-squared calculation of step 42 may be based upon calculating thevariances/covariances across the selected frequency ranges. TheT-squared statistic can be calculated at each time point, e.g.,generating 1024 statistics or 2048 statistics (depending upon thesampling rate) for each second of EMG data. Of course, other suitablesample rates may also be used as appropriate, for example, to avoidaliasing. Using three frequency ranges may result in a 3 by 3 positivesymmetric variance/covariance matrix that may be inverted using aSingular Value Decomposition (“SVD”) calculation. Thevariance/covariance matrix may thus be decomposed into its eigenvaluesand orthonormal eigenvectors. The eigenvalues and eigenvectors may beused to calculate the inverse of the variance/covariance matrix and usedfor the T-squared calculations, such as by calculating weightingcoefficients. The T-squared statistic may, for example, be used tocombine the power amplitude of the three frequency ranges into a singleT-squared statistical value.

In some embodiments, the T-squared calculation of step 42 may assigndifferent weights to the signals from the selected frequency bands. Theweighting factors applied in the step 42 may, for example, have beendetermined by analyzing the selected frequency bands during areference-training period. The weight assigned to each of the frequencybands may, for example, be calculated from the inverse of thevariance/covariance matrix of the selected frequency bands, such as maybe calculated during the reference-training period. The weights assignedto each of the frequency bands may therefore be dynamically adjusted,either automatically such as during periodic normalization (andre-training) or, in some embodiments, by an operator. In this approach,a frequency range exhibiting more muscle activity (usually a lowerfrequency range) may be assigned less weight than a frequency rangeexhibiting lesser muscle activity (usually a higher frequency range).Appropriate weighting may depend upon an individual's normal musclemovement observed during a reference-training period and the particularfrequency bands selected. For example, three bands may be selected asfollows: Range 1 may range from about 300 Hz to about 400 Hz, Range 2may range from about 130 Hz to about 240 Hz, and Range 3 may range fromabout 30 Hz to about 40 Hz. When using those bands, the weightingfactors may be about 5% to about 20%, or about 8% to about 15% for Range3, about 20% to about 40%, or about 25% to about 35% for Range 2, andabout 50% to about 70%, or about 55% to about 65% for Range 1.

The T-squared statistic may be recalculated with any given interval ofdata collection, e.g., sampling frequency for an electrode, and it maybe desirable to smooth the calculated value, such as using exponentialsmoothing, and compare the smoothed statistical value with referencedata. Any of various suitable smoothing techniques (e.g., moving averagefilter, Savitzky-Golay filter, Gaussian filter, Kaiser Window, variouswavelet transforms, and the like) may also be used. Generally, asmoothing factor alpha (α) may range from 0 to 1, i.e., 0<α≦1. In someembodiments, a smoothing factor of 0.5 may be used. Generally, α>0.5 mayreduce the smoothing, and α<0.5 may increase the smoothing. Increasedsmoothing may result in identification of fewer seizure events, thuspotentially making the seizure alarms less responsive. A T-squaredstatistical value may be calculated at each interval of time in datacollection and compared to a reference value (see step 46 below). Thevalue compared to any given reference value may be a smoothed valueand/or may be an average value from a certain time interval. Forexample, data collected over a period of about 10 milliseconds (whichmay include multiple T-squared calculations), may be averaged togetherand the average T-squared value compared to a reference T-squared value.Therefore, the number of comparison calculations between a T-squaredvalue and a reference T-squared value (see step 46) may be the same ordifferent from the number of times the T-squared statistic iscalculated.

In some embodiments, a T-squared value may be an average value from aplurality of T-squared calculations or a discrete T-squared value. Bycollection of a plurality of T-squared values a standard deviationduring a monitoring period (and/or another metric related to thevariability of a data set) may be calculated. Such should not beconfused with the standard deviation of a T-squared reference orbaseline level (and scaling a T-squared calculation in units of standarddeviations of a baseline or reference level—such as by evaluating aZ-factor). The standard deviation of a plurality of discrete T-squaredcalculations may, in some embodiments, be used as a metric in thedetermination of whether to initiate an alarm. Thus, in someembodiments, a T-squared value (such as a discrete value, averageT-squared value or smoothed value), an alarm lag, a standard deviationof a plurality of T-squared values from a monitoring period, or anycombination thereof, may be used to assess whether a seizure may beoccurring.

Referring back to FIG. 4, in a step 44, the reference data (forsubsequent comparison in step 46 with statistical value or valuesdetermined in step 42) may be determined or selected for use. Thereference data may, in some embodiments, be selected from memory, suchas by accessing a template file that may include a predeterminedreference value. For example, a reference T-squared statistical valuemay be established from EMG data obtained during either areference-training period, such as, for example, may have been conductedin a supervised setting, or may be selected for an individual of acertain patient demographic. In some embodiments, the reference valuemay be selected once (such as at the beginning of a monitoring period)and may simply be used in the many different calculations duringcontinuous monitoring. However, in some embodiments, the referenceT-squared value may be determined (and not simply selected) during agiven monitoring period. For example, the apparatus may automaticallyself-train for a patient by calculating the variance/covariance matrixfor the reference T-squared statistic using EMG data obtained when anindividual is initially connected. For example, EMG data from a 10minute period, such as minutes 2-12, may be used for training. Othersuitable time periods may also be used, such as, e.g., one minute, twominutes or five minutes. Such re-training may help the system in dealingwith amplitude variation between periods of use. For example, theamplitude may vary between uses depending upon, e.g., placement of theEMG sensors. The apparatus may be normalized from session to session toaccount for, e.g., how tightly the EMG sensors are placed over a muscle,humidity, EMG sensor location, etc. In some embodiments, the system maydelay for some lag period, e.g., two minutes or some other period, afterconnection of the electrodes before training. An individual may, e.g.,tend to move their arms in a disproportionate manner after placement ofa detection unit on the skin and the impedance of the skin may tend tostabilize over some period following attachment. During a trainingperiod (or re-training) an individual may be substantially at rest. Insome embodiments, an individual may participate in most common dailyactivities during training, with the exception that strenuous activitiesmay be avoided. While executing training, a detection system may also bemonitoring for seizure activity. For example, during any given training(or re-training) period, the system may default to a stored setting,e.g., the system may default to a setting stored in a template file orto the last accepted value that had been used. If a seizure episodeoccurs during the training time, the seizure may be detected and thetraining could be moved to another interval.

In some embodiments, normalization of the system may occur periodically,such as, e.g., at regular intervals. Normalization of the system mayalternatively or also occur when a sufficient increase or decrease inthe average amplitude of the signal occurs and there has been no alarmin a previous time period, e.g., the preceding hour. In someembodiments, re-normalization may be performed at any time where theaverage amplitude changes by more than some rate. For example, a maximumacceptable drift in the amplitude of EMG signal may be denoted in atemplate file (e.g. Max. Amplitude Drift between calibrations).

In some embodiments, re-normalization may be performed at any time thatfalse alarms are deemed to occur too often. For example, in someembodiments, an individual may be alerted that an alarm is being sent oris about to be sent. An individual, if alert and aware that they are infact not experiencing a seizure, may be given the option of sending amessage to a caregiver and/or to a data storage unit that a falsepositive was alerted by the system. In some embodiments, repetitivesignaling that a false positive has been made may serve to initiatesystem re-calibration. In some embodiments, re-calibration may also becontrolled manually, such as by the individual who is being monitoringor by a caregiver, e.g., an individual who may be remotely monitoring apatient. Following completion of re-normalization, reference valuesdetermined therein may be used in any subsequent comparison withstatistical values determined from the EMG data.

In a step 46, one or more statistical values of the processed EMG data,such as, for example, a T-squared statistical value based on poweramplitudes in different ranges (see step 42), may be compared to areference value. The difference in T-squared statistical values(ΔT-squared) may, for example, be calculated as:ΔT-squared=(T-squared statistic)−(ref. T-squared)  (Equation 1)where:

-   -   ΔT-squared=Difference between a T-squared statistical values        calculated during a monitoring period and a reference T-squared        value    -   T-squared statistic=Hotelling's T-squared statistical value        determined from data obtained in a monitoring period    -   Ref. T-squared=Hotelling's T-squared statistical value        determined from data obtained in a reference-training period or        from data of a certain patient population

As previously described, in some embodiments, the reference T-squaredvalue may be adjusted, such as during periodic normalization (such as bydetector normalization and system training or re-training) to be a valuethat is low. Thus, in some embodiments, a reference T-squared value maybe included in the calculation of ΔT-squared and, in some embodiments,may be assumed to be zero.

In a step 48, the determined statistical values and/or the comparison ofthose values with reference values may be used to determine thelikelihood that a seizure event may be occurring. In some embodiments, aZ-value, e.g., units of standard deviation in which statistical factors(T-squared values) differ, may be determined. For example, the Z-valuemay be calculated as follows:Z-value=[ΔT-squared]/[(σ) ref. T-squared]  (Equation 2)where:

-   -   Z-value=Difference between T-squared statistical values scaled        in units of standard deviations    -   ΔT-squared=Difference between T-squared statistical values        calculated from data obtained during the monitoring period and        calculated from data obtained in a reference training period    -   (σ) ref. T-squared=standard deviation of the T-squared baseline

The Z-value may therefore scale the value of ΔT-squared in units ofstandard deviations from a T-squared baseline determined during areference period. The standard deviation of a T-squared baseline usedfor scaling Z-factors may be determined from a reference period (or areference period consecutive with) that is also used as areference-training period used for calculation of weightingcoefficients. In other embodiments, the reference periods may not beconcurrent with a reference-training period used for determination ofweighting coefficients. For example, a reference period where a patientis undergoing certain activities may be used to determine a standarddeviation and may be executed in a supervised setting, and a referenceperiod for establishing weighting factors may be executed during dailyhome use. In some embodiments, a threshold T-squared value may be alsobe used and comparison of a T-squared value may be made to a thresholdT-squared value (such as determined using an MVC or determined based onone or more demographic criteria).

If the Z-value is found to exceed a threshold Z-factor, a decision toinitiate an alarm protocol may be made. For example, an assessment ofwhether to initiate an alarm may be:If Z-Value>threshold Z-factor(initiate alarm protocol)  (Equation 3)If Z-Value<threshold Z-factor(do not initiate an alarm)  (Equation 4)

In some embodiments, the sensitivity of the alarm to the magnitude ofthe T-squared statistics could be adjusted. The threshold Z-factor usedto trigger an alarm condition may be set to a higher value. A higherthreshold Z-factor may serve to reduce the frequency of false alarms. Athreshold Z-factor may, for example, be one standard deviation from thebaseline T-squared value. Thus, a Z value of 1 is one standard deviationfrom the T-squared baseline determined during the training period, a Zvalue of 2 is two standard deviations from that baseline, a Z value of 3is three standard deviations from that baseline, and so forth. Increasedmuscle activity (higher amplitude EMG signals) resulting from a seizuremay produce a larger T-squared value, and a user may, for example, set athreshold Z-value at 3 to ensure that muscle activity must reach acertain amplitude level before an alarm condition is reached. Generally,lower values of threshold Z-values may create more false alarms, andhigher threshold Z-values may be used to reduce the frequency of falsealarms.

The selection of a suitable threshold Z-factor may depend upon thesettings used. For example, weighting factors for the selected frequencybands may influence the response of the system to motor manifestations,noise sources, and seizures. In addition, system training andcalculation of the variance/covariance matrix for calculation of theT-squared values may influence the magnitude of the threshold Z-factor.For example, if a weighting factor for a low frequency band is high, thesystem may be more responsive to some motor manifestations (such as withtypical muscle activity). In addition, for such weighing factors theT-squared statistic may not change as rapidly when higher frequencysignals are sent to maintain the muscle contraction. Decreasing theweight associated with the low frequency band may increase the systemsensitivity to high frequency motor manifestations. However, lowfrequency weighting factors that are too low may result in attenuationof overall signal strength.

Use of frequency bands and relative weighting factors as describedherein may produce T-squared statistics that yield highly stablebaselines (even when an individual is performing some common dailyactivities). Importantly, such may provide T-squared values thatincrease rapidly and selectively to the types of muscle activity thatmay be present when a seizure occurs and thus high threshold Z-factorsmay be used. Such may be advantageous because false positive detectionsmay be greatly reduced, even for individuals who are mobile and/orindividuals who may engage in rigorous daily activities. In someembodiments, threshold Z-factors may be as high as about 1000 yet maystill capture seizure activity. Accordingly, threshold Z-factors hereinmay, in some embodiments, be up to about 1000 or even higher, or may beabout 45 to about 1000.

In some embodiments, an MVC calculation maybe used to set the thresholdZ-factor. For example, the threshold Z-factor may be set such that aT-squared value, in order to exceed the threshold Z-factor, is somefactor (N) of the difference between T-squared statistical valuescalculated during MVC (reference T-squared MVC) and while the individualis at rest. For example, to set the threshold Z-factor to a factor (N)between T-squared statistical values, the threshold Z-factor may becalculated as:threshold Z-factor=N[((ref. T-squared-MVC)−(ref. T-squared))/(σ) ref.T-squared]

The value of a threshold Z-factor, alarm lag or combinations thereof maybe used in assessment of whether a seizure event may be occurring. Ifthe system deems that a seizure may be occurring, the system mayinitiate an alarm protocol (step 50). Of course, in determining whetheran alarm is initiated, the system may store in memory and analyze anynumber of Z-values. For example, if the alarm lag is set to be 2seconds, Z-factors from a suitable number of preceding measurements maybe considered. Alternatively, the system may deem that a seizure is notpresent and, for example, collect a next cycle of EMG data and repeatassessment of whether a seizure may be occurring.

As shown in FIG. 4, steps 46 and 48 describe comparison of processed EMGdata to reference data (or valued derived from that data) and using thatdata to determine if a seizure may likely be occurring. As previouslynoted, reference data may be selected from a template file, and apatient may not need to re-train the detection unit every day. Rather, auser may attach the detection unit, and established reference data maybe downloaded and used to analyze EMG derived statistical values. Atemplate file, may, for example, include various constants used ineither of steps 46 and 48 to treat or analyze collected data. Some ofthe information that may be included in such a template file is shown inTable 1. For clarity, the “XX” is simply a placeholder, and should notbe construed to connote magnitude or precision in any way.

TABLE 1 List of some constants that may be found in a template fileVariable Value/unit Type Threshold Z-value XX (standard Criterion foranalysis deviations) of alarm state Alarm lag setting XX (seconds)Criterion for analysis of alarm state Smoothing factor (α) XX Dataprocessing factor Bandpass filter width XX (seconds) Selection routineReference T-squared XX Data processing factor Default Value StandardDeviation of the XX Data processing factor T-squared reference Max.Amplitude Drift XX (mV) Selection routine between calibration Max.Amplitude Drift Rate XX (mV/sec) Selection routine between calibrationFrequency range for band 1 XX (Hz) Selection routine Frequency range forband 2 XX (Hz) Selection routine Frequency range for band 3 XX (Hz)Selection routine Frequency range for band N XX (Hz) Selection routineWeighting factor Region 1 XX Data processing factor Weighting factorRegion 2 XX Data processing factor Weighting factor Region 3 XX Dataprocessing factor Weighting factor Region N XX Data processing factorAlgorithm Type XX Selection routine Filter window length XX (seconds)Selection routine

It is envisioned, at least in some circumstances, that at least some ofthe initially selected values for a template file may be established bygrouping an individual into a patient demographic. For example, in someembodiments, an initial template file may be obtained using historicaldata from a general patient demographic. A patient may, for example, bedefined by various characteristics including, for example, anycombination of age, gender, ethnicity, weight, level of body fat, fatcontent in the arms, fat content in the legs, mid upper armcircumference, fitness level, level of one or more maximum voluntarycontractions, or the patient may be defined by other characteristics.The patient's medical history including, for example, history of havingseizures, current medications, or other factors, may also be considered.To establish settings for a given patient based on the particularpatient demographic to which that patient matches archived data forother patients in that particular demographic may be used. The archiveddata may include EMG data together with an indication of whether or notthe data is associated with a non-seizure or seizure period. As data iscollected for a given patient, that data may be added to the datalibrary. Once a template file is generated or selected, it may beincluded in computer memory within a detection unit, base unit, or both,and an individual may use the detection unit in a home-setting.

Settings for the device may also be established from patient monitoringduring a training period, e.g., the patient may be monitored in asupervised setting. For example, during monitoring data may be collectedfor determining general seizure characteristics. The patient may, forexample, be monitored with EMG over a period of several days, or someother interval, as necessary to collect data associated with astatistically significant number of seizures. During the period ofmonitoring, patient EMG data may be collected. EMG data from timeperiods with known seizures and also intervals with non-seizure periodsmay be collected, archived, and an operator may analyze the data anddetermine a reference value for use. An operator may, for example,analyze the data and may import various different hypothetical valuesinto a template file. The system may then, using the hypotheticalvalues, simulate, e.g., whether any alarms or false positives would havebeen issued based on the recorded data. The operator may select valuesfor a template file based on the data or based on values typical for allpatients or for patients of a certain demographic.

In some embodiments, a reference training period may also involvemeasurement of the Maximum Voluntary Contraction (MVC) of a patient ormeasurement of the electromyographic signatures thereof. For example, apatient may execute a voluntary contraction under conditions of maximumeffort and the electromyographic signal recorded. As the strength ofmuscle varies from individual to individual, the amplitude of EMGsignals may vary as well; therefore, having the patient perform the MVCmay tailor device settings to that individual's musculature. The MVC maybe used to assist the operator in setting sensitivity values, such asthreshold Z-value and/or alarm lag, for a patient. The MVC may beparticularly useful for patients who experience seizures onlyinfrequently and for whom it may be difficult to gatherseizure-significant-statistical data for seizures during a reasonabletraining period.

In some embodiments, an apparatus and method of detecting seizures usingEMG signals could include a number of steps. For example, in someembodiments, the detection unit and/or base station may be placed in a“sleep” mode until signal activity warrants continuous monitoring. Whenin “sleep” mode, the base station may periodically poll the detectiondevice for leads-off detection and signal monitoring. Such embodimentsmay be used to organize the collection of portions of data that are mostrelevant, e.g., portions of data most likely to include a seizure.

For example, FIG. 5 illustrates one embodiment of a method 58 ofdetecting seizures. In method 58, the rate of data collection dependsupon the state of the detection unit. Method 58 may, for example, beused to toggle a detection unit and/or base station between a “sleep”mode, i.e., characterized by operations within dashed line 60, and amode of substantially continuous operation, such as active state 66. Asshown in FIG. 5, a detector and/or base unit may be configured to existin the resting state 62 for a portion of time while in a “sleep mode.”While in the resting state 62, a detector or base unit may be silent,e.g., it may not be monitoring or collecting data from a patient. Theresting state may include instructions to periodically exit the restingstate 62 and, for example, collect detector data for a period of time.That is, a detector may enter a polling operation step 64 where data iscollected. The duration of an individual polling operation may besufficient to collect data as needed to make a decision regarding thestate of the detection unit. That is, for example, based on datacollected during polling step 64, a detection unit may revert back tothe resting state 62 or may enter another state, such as active state66. Once placed in active state 66, the detection unit may, for example,monitor electrical activity in a continuous or substantially continuousmanner.

For example, if threshold Z-value and alarm lag thresholds are exceeded,then an alarm may be sent, e.g., to the base station together with data.The base station may separately process the data for verification of thealarm condition. If the base station agrees with the alarm, then thebase station may generate an alarm to remote devices and local soundgenerators. An alarm or alert may comprise an audible signal, orpre-recorded voice message, or a text message, or email, or triggervibration in a PDA, or other suitable attention-getting mechanisms. Thepatient's detection unit or base station may further comprise GPStechnology, such as that used in smartphones and handheld navigationdevices, to allow a caregiver to determine the patient's location. Analarm or alert may comprise patient location information.

In other embodiments, a unitary device may be used without the need fora base station or other remote data processing capabilities. The unitarydevice may process the EMG data and send an alarm to a caregiver whenthe threshold Z-value and alarm lag thresholds are exceeded. A unitarydevice may comprise a small, battery-powered mobile device attachableover a patient's muscle that may communicate an alarm to a caregiver vianetwork communication, such as cellular telephony. The unitary devicemay further comprise GPS technology to allow a caregiver to determinethe patient's location. Again, the alarm or alert may comprise patientlocation information.

In some embodiments, a detection unit may (if appropriately triggered)send an alarm to a base station (such as by 802.11 protocols) and alsosend communication from the patient worn detection unit to a remote cellphone or other hand-held device via Bluetooth™. The communication mayalso, e.g., direct the cell phone to dial out to a predetermined phonenumber, e.g., by sending a txt message, and if a person answers on theother end answers, to open a voice-comm link. Thus, various functionsthat may be executed by a base station may also be executed by a cellphone. One advantage of having both a base station and cell phonecontacted, i.e., via Bluetooth™, is that the two transmission means mayhave different working distances. Thus, a patient may be free to movebetween locations without risk of losing a monitoring connection.

In some embodiments, having the base station (or smart phone with asuitable installed application) agree to the detection unit's alarmintroduces a voting feature. With such a feature, both devices must voteon the decision and agree to trigger the alarm. This process may limitfalse alarms. In such embodiments, the detection unit may process thesignal data, and send both the results and the raw data to a basestation. The base station may independently process the signal data andcompare its results to the results sent by the detection unit. As notedabove, the base station may have greater processing power, and thus, beable to process more time periods than the detection unit and to do sowith more advanced algorithms. If the base station results match thoseof the detection unit, then an alarm may be generated, e.g., a messagesent to a caregiver. If the base station results differ from those ofthe detection unit, the detection unit's results may be flagged as afalse positive. Initially, the detection unit results may be given aweight that is more likely to trigger an alarm (even if contradicted bythe base station), and if false positives are generated and as operatorfeedback is provided, the method may adapt to the patient to reducefalse positives, such as by adjustment of Z-factor settings, lagsettings or weighting coefficients. As the method adapts, the basestation results may be given more confidence.

In some embodiments, a detection unit may use an algorithm thatcalculates a T-squared statistical value. Comparison of the T-squaredstatistical value to a reference T-squared statistical value (anddetermining a Z-factor, e.g., the number of standard deviations by whichthe T-statistical value differ) may be used to monitor the patient'sstate. The algorithm may further use lag settings as needed to adjustthe system sensitivity. To simplify the detection unit calculation, alimited number of frequency regions may be selected and a limited numberof weighting factors may be used in the detector unit calculation. Thebase station, which again, may have greater processing power, may, insome embodiments, apply a number of different algorithms including,e.g., variations of frequency regions, weighting factors and othercriteria, to evaluate whether a seizure may be occurring. The basestation may also apply an algorithm that, e.g., uses a T-squaredstatistical value to log an event in a register. Additional registersmay be associated with other data characteristics, such as, e.g., thepresence of a characteristic GTC waveform, the presence of data bursts,or to other characteristics, such as described in U.S. application Ser.No. 13/275,309. Analysis of events logged in registers for differentcharacteristics of the output data may be used to assess whether aseizure incident is declared and whether an alarm is sent to one or morelocations.

During or after a seizure event, a human operator may review and adjustthe threshold Z-factor and alarm lag settings based upon the severity ofthe seizure or possibly the non-detection of an actual seizure becauseof too-sensitive settings. Many people have seizures and do not realizethat they had a seizure, e.g., the short-lived seizures discussed above.Having stored data to review may help medically manage the person withseizures. Also, a human operator may evaluate the data and, based onactivities known to have occurred during the time of monitoring,conclude that a seizure did not occur, and either cancel the alarm orinstruct the monitoring system that the detected waveform did notindicate a seizure. Likewise, a human operator may instruct themonitoring system that an undetected seizure had occurred by, e.g.,specifying the time during which the seizure occurred. For example, anoperator may be provided with EMG data which comprise a rolling “window”of EMG activity, and the human operator may “rewind” the recorded signaland indicate to the monitoring system the time window in which theseizure occurred. In some embodiments, the base station may visuallydepict the signal and provide a graphic user interface (GUI) that allowshuman operators to accomplish the “window” selection and define otheroperating thresholds and conditions. The monitoring system may thus haveadditional data points against which to evaluate future seizure eventsfor that particular patient.

An apparatus for detecting seizures may be man-portable, and may includea detection unit that may be attached to the body, such as by use of anelastic arm band. The detection unit may be battery powered, and maywirelessly communicate with the base station. The detection unit mayinclude sufficient data storage, processing and transmission capabilityto receive, buffer, process and transmit signals. The detection unit mayprocess the signals and conduct a simplified analysis, e.g., using fewerT-squared calculations or PCA components. When the detection unitdetermines that a seizure is occurring, it may download both itsanalysis and the raw signal data to a bedside base station for morecomplex processing. The base station may have much more power, largerstorage capability and greater processing speed and power, and be betterequipped overall to process the information. It could, for example,perform more T-squared calculations. Likewise, the base station maytransmit raw and processed signal data to a remote computer for furtheranalysis and aggregation with signal data from other units in use. Forexample, multiple base stations may transmit data for multiple patientsto a remote computer. Each base station may not receive the other basestation's data, but the remote computer may serve as a common repositoryfor data. Aggregation of the data may allow further data points fromwhich to further refine the baseline thresholds, alarm sensitivitythresholds and statistical information that may be supplied to basestations and detection units as a factory default or upgrade.

The system described herein may, in some embodiments, be configured toallow a caregiver to monitor and/or adjust various system settings. Forexample, the system may be accessed by caregivers at the point ofmonitoring, and used, e.g., to modify system constants (e.g., adjustsensitivity), review the patient history, family history, current orpast medications, compliance to a medication regimen, or create a log ofseizure incidents to help medically or surgically manage the patient.Exemplary aspects of some embodiments of a system interface, areillustrated in the exemplary FIGS. 6, 7A-7D, 8, 9A-9D, 10A-10D and 11.For example, the base station may be programmed to provide a userinterface to allow a caregiver to select various processing, analysis,alarm and other options. A base station may, for example, provide acaregiver with a graphical user interface similar to that of FIG. 6which may allow a user to both record, process and analyze EMG data asit is collected in real-time, or process and analyze previously-recordedEMG data. As may be seen in the embodiment of FIG. 6, a user may be ableto enter in the Client field the name or ID number of the patient to bemonitored or whose records are to be reviewed. In the Date field, theuser may enter the date of monitoring. In the EMG Sensor field, the usermay select the number of EMG sensors that are to be used (such as 1 or 2sensors). In addition, the user may adjust the system's sensitivity,such as, e.g., by setting the threshold Z-value and alarm lag settings,such as in quarter-second intervals or in some other interval. In someembodiments, the threshold Z-factor may range from 1 to 500 or more. Insome embodiments, an adaptive algorithm may use feedback from the userregarding false alarms to automatically adjust the Z-factor and/or alarmlag to reduce sensitivity, e.g., by incrementing the values together oralternately.

In addition, in some embodiments, a user may be able to place the systemin training mode. For example, the software may give the user an optionto select the Learn button and the user may identify a time at which thelearning period should start. In addition, the user may, e.g., automatethe learning by selecting the “Auto” button or prompting the system tore-train in some other way. The user may save the settings by selectingthe Save button. A user may start the monitoring and recording processby selecting the Start button, and pause and resume the monitoring andrecording process by selecting the Pause and Continue buttons,respectively. In brief, the system may generally allow a user tomanually adjust or select a given routine from within the systemfunctionality.

In addition, the system described herein may include functionality tosearch for and/or download previous recordings. For example, if a userdesires to process a previously recorded set of EMG signals, such asopening a previously-recorded file, or by looking at earlier portions ofcurrent monitoring sessions, a user may do so. A user may, for example,open or close a previously-recorded EMG file through a drop-down menuunder File, similar to that illustrated in FIG. 7A. By default, thepatient information fields in the Data box of the embodiment of FIG. 6may be blank. If a user opens an EMG file, those fields may beautomatically populated, as illustrated in FIG. 7B. As illustrated inFIG. 7B, an EMG file may be selected and opened, e.g., the EMG fileselected in FIG. 7B is for recording 075950 of patient A begun on Jan.2, 2011, at 10:36 pm (the EMG file of which is discussed in relation toFIG. 12). In this example, the complete file comprises approximately 8hours of data and contains over 58 million records. A user may select,for example, only the first 8 million records for extraction if, forexample, it is known that an episode on that day occurred only oncestarting approximately 50 minutes into the monitoring session, andlasted for approximately 15 minutes. Alternatively, a user may specifythat the data should be processed all the way to the last record. Inother embodiments, a user may specify a time range from which to processdata, as may be seen in the embodiment of FIG. 8C.

A user may, in some embodiments, export an EMG file to another programfor additional processing, as may be seen in the embodiment of FIG. 7D.For example, after reading an EMG file and running an alarm analysis,the data may be exported to a tab-delimited text file that can beimported into other software for analysis and graphing, such as STATA,SAS and JMP. Users may, for example, export alarm data comprising twocolumns: the T-squared value (or PCA value) for each record, and thealarm state. The alarm state may, for example, be depicted by a value ofeither zero or one. A value of one may indicate that the alarm wassignaled at a certain time. The EMG value(s) may also be exportedwithout prior processing.

In addition, the EMG values together with the extracted frequency bandscan be exported. For example, if two channels, e.g., EMG27 and EMG28,are being used, data from those channels may be exported and eachfrequency band may have two values, for example, one for each of the twoEMG sensors. A user interface may also allow a user to open and closelog files, as may be seen in the embodiment of FIG. 8. The base stationmay automatically open and close a log file, log.txt, that can be viewedwith a text processor. The Log portion of the main menu may allow a userto rename the log file and select where the log file should be stored. Auser may also close that file and open another log file at any time. Logfiles may be used to debug problems that occur using the software.

A user interface may also provide a Configure menu, as may be seen inthe embodiment of FIG. 9A. The Configure portion of the main menu mayallow a user to test the effectiveness of alternate algorithms. Bydefault, Hotelling's T-squared statistic may be selected. Alternatively,a PCA analysis may be conducted. The absolute values of the extractedfrequencies may be processed instead of the actual value. The extractedfrequency values may generally oscillate around zero with about half ofthe values being negative. By default these negative values may be usedto calculate the T-squared and PCA statistics. However, by selecting“absolute values” all negative values may be replaced with theirabsolute, non-negative value.

For example, in the embodiment of FIG. 9B, three signal frequency bandsmay be selected for processing. By default, three frequency bands may beautomatically selected: 300-400 Hz, 130-240 Hz and 30-40 Hz. Moreover,additional bands, such as, e.g., two additional bands may be selectedfor other frequency ranges.

Additionally, as may be seen in the embodiment of FIG. 9C, a user mayselect the width of the bandpass filter. By default, 0.25 seconds may beused for the width of the filter. If you select 0.25 seconds you may beselecting 0.25 seconds on either side of a given data point. Moregenerally, the width of a filter may range from about 0.10 seconds toabout 0.50 seconds. Therefore, the filter may use 1025 records for eachfilter calculation, corresponding to 0.50 seconds of data within asample frequency of 2048/sec.

As may be seen in the embodiment of FIG. 9D, a user may select aparticular patient recording. Selecting a patient recording may allowthe system to use prior patient recordings to configure a system foranother recording for that patient. These selections may automaticallytailor the start and stop record numbers, and the sensitivity values tothe best found for that recording or for that patient.

The user interface may also allow a user to view various data byinteracting with the View menu. As may be seen in the embodiment of FIG.10A, the Client Information menu item may provide a user withinformation related to a patient, such as the information shown in FIG.7B. Additionally, episode information may be viewed, if any, as may beseen in the embodiment of FIG. 10C. If a record contains one or moreseizure episodes, the start and stop records of those episodes may bedisplayed to the user. Alternatively, the start and stop times of thoseepisodes may be displayed to the user.

The View menu may also provide a user with a way to view alarminformation, as may be seen in the embodiment of FIG. 10D. In someembodiments, a user may only select the Alarm Information menu itemafter running an alarm analysis. The alarm analysis may process theentire data with the signal processing configuration specified, e.g.,with Hotelling's T-squared. The alarm analysis may display a popupwindow that summarizes the alarms found. If a user closes that windowand later wants to view it again, the user may select View→AlarmInformation.

Generally, the devices of a seizure detection system may be of anysuitable type and configuration to accomplish one or more of the methodsand goals disclosed herein. For example, a server may comprise one ormore computers or programs that respond to commands or requests from oneor more other computers or programs, or clients. The client devices, maycomprise one or more computers or programs that issue commands orrequests for service provided by one or more other computers orprograms, or servers. The various devices in FIG. 1, e.g., 12, 13, 14,16, 17, 18 and/or 19, may be servers or clients depending on theirfunction and configuration. Servers and/or clients may variously be orreside on, for example, mainframe computers, desktop computers, PDAs,smartphones (such as Apple's iPhone™, Motorola's Atrix™ 4G, and ResearchIn Motion's Blackberry™ devices), tablets, netbooks, portable computers,portable media players with network communication capabilities (such asMicrosoft's Zune HD™ and Apple's iPod Touch™ devices), cameras withnetwork communication capabilities, wearable computers, and the like.

A computer may be any device capable of accepting input, processing theinput according to a program, and producing output. A computer maycomprise, for example, a processor, memory and network connectioncapability. Computers may be of a variety of classes, such assupercomputers, mainframes, workstations, microcomputers, PDAs andsmartphones, according to the computer's size, speed, cost andabilities. Computers may be stationary or portable, and may beprogrammed for a variety of functions, such as cellular telephony, mediarecordation and playback, data transfer, web browsing, data processing,data query, process automation, video conferencing, artificialintelligence, and much more.

A program may comprise any sequence of instructions, such as analgorithm, whether in a form that can be executed by a computer (objectcode), in a form that can be read by humans (source code), or otherwise.A program may comprise or call one or more data structures andvariables. A program may be embodied in hardware or software, or acombination thereof. A program may be created using any suitableprogramming language, such as C, C++, Java, Perl, PHP, Ruby, SQL, andothers. Computer software may comprise one or more programs and relateddata. Examples of computer software include system software (such asoperating system software, device drivers and utilities), middleware(such as web servers, data access software and enterprise messagingsoftware), application software (such as databases, video games andmedia players), firmware (such as device specific software installed oncalculators, keyboards and mobile phones), and programming tools (suchas debuggers, compilers and text editors).

Memory may comprise any computer-readable medium in which informationcan be temporarily or permanently stored and retrieved. Examples ofmemory include various types of RAM and ROM, such as SRAM, DRAM, Z-RAM,flash, optical disks, magnetic tape, punch cards, EEPROM. Memory may bevirtualized, and may be provided in, or across one or more devicesand/or geographic locations, such as RAID technology.

An I/O device may comprise any hardware that can be used to provideinformation to and/or receive information from a computer. Exemplary I/Odevices include disk drives, keyboards, video display screens, mousepointers, printers, card readers, scanners (such as barcode,fingerprint, iris, QR code, and other types of scanners), RFID devices,tape drives, touch screens, cameras, movement sensors, network cards,storage devices, microphones, audio speakers, styli and transducers, andassociated interfaces and drivers.

A network may comprise a cellular network, the Internet, intranet, localarea network (LAN), wide area network (WAN), Metropolitan Area Network(MAN), other types of area networks, cable television network, satellitenetwork, telephone network, public networks, private networks, wired orwireless networks, virtual, switched, routed, fully connected, and anycombination and subnetwork thereof. The network may use a variety ofnetwork devices, such as routers, bridges, switches, hubs, repeaters,converters, receivers, proxies, firewalls, translators and the like.Network connections may be wired or wireless, and may use multiplexers,network interface cards, modems, IDSN terminal adapters, line drivers,and the like. The network may comprise any suitable topology, such aspoint-to-point, bus, star, tree, mesh, ring and any combination orhybrid thereof.

Wireless technology may take many forms such as person-to-personwireless, person-to-stationary receiving device, person-to-a-remotealerting device using one or more of the available wireless technologysuch as ISM band devices, WiFi, Bluetooth, cell phone SMS, cellular(CDMA2000, WCDMA, etc.), WiMAX, WLAN, and the like.

Communication in and among computers, I/O devices and network devicesmay be accomplished using a variety of protocols. Protocols may include,for example, signaling, error detection and correction, data formattingand address mapping. For example, protocols may be provided according tothe seven-layer Open Systems Interconnection model (OSI model), or theTCP/IP model.

Additional information related to the methods and apparatus hereindescribed may be understood in connection with the examples providedbelow.

Example 1

In one example, data for an individual, i.e., patient A, was recorded.Data from that recording may be accessed remotely or may be accessedthrough a user interface configured on a base station. For example, asshown in FIG. 11, the base station software enables a user to access andreview information. As shown in FIG. 11, the interface may allow a userto select the patient's records, e.g., first ˜8 million from the EMGfile 075950, for display. The records may be selected and displayed, asshown in FIG. 12. In this particular example, electrodes were connectedto a patient during a period where the patient was particularly prone toexperiencing a seizure. For the recording illustrated in FIG. 12, a10-minute training period was started 2 minutes into the monitoringsession. In FIG. 12, a first vertical line 102 to the right of theY-axis has been drawn at approximately 12 minutes to denote the end of atraining period. FIG. 13 shows the log periodogram of the trainingregion. As indicated therein, and as evident by comparison of FIG. 13 toFIG. 14 (which shows the log periodogram of an episode region), onlyrelatively low amplitude signals are present across a majority of thespectrum.

Referring back to FIG. 12, the period between vertical line 102 andvertical line 104 denotes a period where EMG data was collected withoutinitiation of an alarm. For illustration purposes, the next threevertical lines roughly mark the start of a seizure episode (line 104),the start of an alarm condition (line 106), and the end of the majorpart of a seizure episode (line 108). As displayed from the base stationuser interface and FIG. 11, Z=4 and the alarm lag is 4 seconds for thisexample. Thus, an alarm condition may be initiated when Z≧4 for a periodof greater than 4 seconds. In this Example the patient was monitored fora relatively short period of time during sleep. Monitoring the patientfor greater periods of time or while the patient may engage in otheractivities, such as where other substantial signals may potentially bepresent, other detection settings, such as with greater values of thethreshold Z and/or alarm lag may be used.

As illustrated in FIG. 13, the log periodogram of the seizure episodeshows significantly higher amplitude as compared to a non-episodeperiod. The x-axis in FIG. 13 is plotted with respect to the naturalfrequency and may be to a frequency in hertz. For example, as may beseen from FIG. 14, frequency ranges from about 300 Hz to about 400 Hz,from about 130 Hz to about 240 Hz, and from about 30 Hz to about 40 Hzmay be particularly suitable for processing and analysis.

As shown in FIGS. 15-21, respectively, the EMG waveform filtered to passseven different frequency ranges: 300-400 Hz, 130-240 Hz, 30-40 Hz,10-50 Hz, 4-5 Hz, 3-4 Hz and 2-3 Hz is shown. As may be seen in FIGS.15-18, the lower frequency ranges of 10-50 Hz, 4-5 Hz, 3-4 Hz and 2-3 Hzmay show much more non-seizure muscle activity than the higher frequencybands for the reasons discussed above, namely, that lower frequencysignals may be used to recruit muscles for activity, and that activitymay or may not increase. Thus, those lower frequency ranges may be lesssuitable for use in detecting seizures and may result in an increasednumber of false alarms if used or given too much weight. Again, forillustration, three vertical lines are provided to mark the start of aseizure episode, the start of an alarm condition, and the end of themajor part of a seizure episode. As may be seen from those waveforms,there may be occasional muscle activity having a high Z value, but thatactivity may not be sustained for a sufficient time to trigger an alarmcondition.

FIG. 22 illustrates the waveform after T-squared statistics has beencalculated across the three frequency ranges of 300-400 Hz (as may beseen in FIG. 15), 130-240 Hz (as may be seen in FIG. 16) and 30-40 Hz(as may be seen in FIG. 17). Again, for illustration, vertical lines areprovided to mark the start and end of the seizure episode. When theT-squared calculation shows a Z value above the threshold of Z=4, thesystem may signal an alarm condition, as may be seen in the embodimentof FIG. 22, and sent an alert to a caregiver. As may be seen in theembodiment of FIG. 23, a chart of the T-squared alarm statisticindicates that the alarm state may be binary, i.e., may be zero exceptwhere an episode is recognized in which case the alarm state may be one.

Example 2

In this Example 2, a total of 12 patients were each monitored for aperiod of about 4.4 days. Monitoring included using surface EMGelectrodes and processing of data using the T-squared algorithm asdescribed herein. The patients included 5 females and 7 male subjects,all of whom were mobile individuals. Of course, patients who arephysically unable to walk may also be monitored. That is, in general,the discrimination between non-seizure motor manifestations and seizureactivity for patients who are capable of movement and engaged in dailyactivities may be most challenging. In this study, the monitoring periodincluded an overnight stay in a supervised setting. In addition to aportion of time where the patients were typically sleeping themonitoring period included a period of time where the patients were freeto execute any of various common daily activities. For example, thepatients were free to move (walk around), brush their teeth, comb theirhair, watch TV or engage in any other desired activity. Those activitieswere verified to have been performed by the patients with videomonitoring, which was also used as a check to verify the presence of anyseizure activity. That is, in addition to monitoring with surface EMGelectrodes, the patients were also monitored with inpatient scalpvideo-EEG (VEEG) recording.

For the EMG recordings, one arm of each patient was fitted to providesurface EMG recordings from muscles of the unilateral biceps and tricepsbrachii. However, while data from two muscle regions was collected, ithas been found that data from either muscle group may be usedindividually. The EMG electrodes were bipolar Ag/AgCl electrodes anddata was collected and streamed wirelessly for evaluation. For all ofthe patients in this study, three frequency bands were selected. Thosefrequency bands include a first band ranging from about 30-40 Hz, asecond band ranging from about 130-240 Hz, and a third band ranging fromabout 300-400 Hz. EMG data was collected at the sampling rate of about1,024 data points per second. Other detection settings were selected bycharacterization of electrode signals during a reference period. Forexample, a threshold T-squared value may be established to be about 100standard deviations above a T-squared reference value derived from atraining period. Such a value may be set by inputting a value of 100 forthe threshold Z-factor. For the patients in this study thresholdZ-factors were set to values between about 100 to about 1000. Alarm lagsettings for the patients in this study were set to be between about 3to about 10 seconds.

During monitoring, 6 of the 12 patients had a total of 7 GTC seizures.The monitoring system successfully detected all of the GTC seizures thatwere present. All of the seizures were detected within no more thanabout 10 seconds of any initial arm movement during a given GTC seizure(and generally within about 2 or about 3 seconds) of this movement. Thesystem therefore provides almost immediate detection of a GTC seizure.Of course, the immediate detection of a GTC seizure may be important.Such may be particularly pertinent, for example, wherein detection of aseizure may be linked to stimulating devices that may be used to abortor attenuate a seizure. In addition, such may enable first responders(such as a parent or other caregiver) to be present as soon as possibleduring a seizure event. In addition, and despite the subjects being freeto move about and conduct common daily activities, the system was ableto detect seizures without initiation of a single false positive alarm.

Example 3

In this Example 3, each of three patients was evaluated for seizureactivity. Each of the patients was mobile and during the monitoringperiod of about 30 hours each was free to engage in common dailyactivities. Prior to the monitoring period, the patients were instructedto execute at least two maximum voluntary contractions. It should benoted that a fit patient may execute a plurality of maximum voluntarycontractions, resting between executions, with little decline in muscleactivity. For other patients, a smaller number of MVC executions may beperformed before the patient becomes tired. Thus, the number of MVCexecutions that are conveniently executed in one reference period may bepatient specific. While executing a MVC, EMG activity was collected anda T-squared value determined. For a first patient, mean T-squared valueswhile executing two separate MVCs were about 742 and 809. For the firstpatient, one seizure was measured during the monitoring period. Thatseizure was found to provide a T-squared value of 2663—or about 3.4times the average value of T-squared measured during the execution ofthe two MVCs. For a second patient, a T-squared value while executing anMVC was about 270. For the second patient, one seizure was measuredduring the monitoring period. That seizure was found to provide aT-squared value of 2386—or about 8.8 times the average value ofT-squared measured during the execution of the two MVCs. For a thirdpatient, mean T-squared values while executing two separate MVCs wereabout 570 and 668. For the third patient three seizures were measuredduring the monitoring period. Those seizures were found to provideT-squared value of 330302, 35767, and 53944. For patients who areactive, a threshold T-squared value of about 100% of the MVC does notresult in a significant number of false positives. For patients who areresting or patients who are physically impaired and not active athreshold T-squared value of about 50% of the MVC does not result in asignificant number of false positives.

Example 4

In this Example 4, a patient may be set up to be monitored in a homesetting and a detection unit may be placed on the biceps and triceps.The monitoring system may include a remote transceiver element andadditional transceivers associated with different locations in thepatient's living space. A first environmental transceiver may be locatedon the patient's bed and a second environmental transceiver may belocated in the patient's bathroom. When the patient is sleeping, adetector unit and first environmental transceiver may be operativelycommunicating. The system base station may receive an indication of thisrelationship and select a template file that is customized for thatpatient. The selected template file may be based on data collected forthat patient while that patient is sleeping. The template file mayinclude a Z lag setting that allows the patient to move while sleepingwithout initiating an alarm.

The patient may wake during sleep and walk to the bathroom. Thismovement may typically result in an elevated level of muscle movement.In addition, as the patient moves away from their bed, the temporalrelationship between the first environmental transceiver and thedetection unit may be altered. The base station may receive anindication of this position-dependent relationship and in responseselect a second template file for use. In this example 4, the secondtemplate file has a Z lag setting and weighting factors (such as adiminished weight associated with low and mid level muscle activity) asappropriate to diminish the sensitivity of the system to an alarm. Aftergoing to the bathroom the patient may return to their bed and thepositional relationship between the detection unit and the firstenvironmental transceiver may be restored. The base station may receivean indication of the new position of the patient, e.g., the firstenvironmental transceiver may send a pulse of information to the basestation once it detects the presence of the detection unit, and selectan appropriate template file, e.g., one associated with a patientsleeping in bed.

In some embodiments, a transceiver may additionally be mounted within aunit of furniture or some other structure, e.g., an environmental unitor object. If a detection unit is sufficiently close to thattransceiver, such a transceiver may be capable of sending data to a basestation. Thus, the base station may be aware that information is beingreceived from that transducer, and therefore the associatedenvironmental unit. In some embodiments, a base station may select aspecific template file, e.g., such as including weighting factorsbetween frequency regions or Z-factors used to evaluate whether an alarmis triggered or alarm lag settings or other data as described furtherherein, that is dependent upon whether or not it is receiving a signalfrom a certain transceiver. Thus, for example, if the base stationreceives information from a detector and from a transducer that isassociated with a bed or crib, it may treat the data differently than ifthe data is received from a transducer associated with another.

Although the disclosed method and apparatus and their advantages havebeen described in detail, it should be understood that various changes,substitutions and alterations can be made herein without departing fromthe invention as defined by the appended claims. Moreover, the scope ofthe present application is not intended to be limited to the particularembodiments of the process, machine, manufacture, composition, ormatter, means, methods and steps described in the specification. Use ofthe word “include,” for example, should be interpreted as the word“comprising” would be, i.e., as open-ended. As one will readilyappreciate from the disclosure, processes, machines, manufacture,compositions of matter, means, methods, or steps, presently existing orlater to be developed that perform substantially the same function orachieve substantially the same result as the corresponding embodimentsdescribed herein may be utilized. Accordingly, the appended claims areintended to include within their scope such processes, machines,manufacture, compositions of matter, means, methods or steps.

We claim:
 1. A method of detecting seizures comprising: detecting EMGsignals for a time period, the time period comprising a reference periodand a monitoring period; using digital filtering to isolate from the EMGsignals spectral data for a plurality of frequencies bands selected fromthe range of about 2 Hz to about 1000 Hz; calculating a first T-squaredvalue, the first T-squared value being determined from spectral data forthe plurality of frequency ranges, from at least one part of thereference period; calculating a second T-squared value, the secondT-squared value being determined from spectral data for the plurality offrequency ranges, from at least one part of the monitoring period;comparing the second T-squared value to the first T-squared value; anddetermining whether to trigger an alarm condition using said comparisonof the first T-squared value to the second T-squared value.
 2. Themethod of claim 1 further comprising: establishing for a patient ademographic to which said patient most closely resembles; and setting athreshold T-squared value based on data of acceptable thresholdT-squared values for said demographic; wherein the comparison of thesecond T-squared value to the first T-squared value includes calculatingthe difference between said second T-squared value and said firstT-squared value; wherein the determining of whether to trigger an alarmcomprises calculation of whether said difference is above said thresholdT-squared value.
 3. The method of claim 2 wherein the patient isclassified based on at least one of criterion, said criterion selectedfrom the group consisting of age, gender, ethnicity, weight, level ofbody fat, fat content in the arms, fat content in the legs, mid upperarm circumference, fitness level, and level of one or more maximumvoluntary contractions.
 4. The method of claim 1 further comprisingassigning weighting coefficients to different bands among said pluralityof frequency bands.
 5. The method of claim 4 wherein the assignment ofweighting coefficients is based on EMG data collected during saidreference period.
 6. The method of claim 4 wherein the weightingcoefficients are calculated from an inverse matrix of avariance/covariance matrix of T-squared data calculated from datacollected during the reference period.
 7. The method of claim 6: whereinthe weighting coefficients calculated during the reference period areused to adjust the contribution of spectral data, for each frequencyamong the plurality of frequency ranges collected during the monitoringperiod, in said calculation of the second T-squared value; wherein theplurality of frequency bands comprises a first frequency band from about300 Hz to about 400 Hz, a second frequency band from about 130 Hz toabout 240 Hz, and a third frequency band from about 30 Hz to about 40Hz; and wherein the weighting coefficients calculated during thereference period are only used as settings if said weightingcoefficients are within an acceptable range.
 8. The method of claim 4wherein the weighting coefficients adjust the contribution of spectraldata, for each frequency among the plurality of frequency rangescollected during the monitoring period, in said calculation of thesecond T-squared value.
 9. The method of claim 1 further comprisingadjusting an amplitude of EMU signals collected at the start of saidreference period, and collecting EMG data for calculation of weightingcoefficients during said reference period.
 10. The method of claim 9wherein the weighting coefficients adjust the contribution of spectraldata, for each frequency among the plurality of frequency rangescollected during the monitoring period, in said calculation of thesecond T-squared value.
 11. The method of claim 1 further comprising:calculating a plurality of T-squared values during said monitoringperiod; and calculating a standard deviation from said plurality ofT-squared values.
 12. The method of claim 11 wherein the alarm conditionis not triggered unless said standard deviation is above a thresholdstandard deviation.
 13. The method of claim 12 further comprising:collecting EMG signals while a patient is executing a maximum voluntarycontraction and measuring a plurality of T-squared values for differenttime points during said maximum voluntary contraction; calculating areference standard deviation from said plurality of T-squared values fordifferent time points during said maximum voluntary contraction; andsetting said threshold standard deviation based on the referencestandard deviation.
 14. A method of detecting seizures with motormanifestations comprising: detecting EMG signals; using digitalfiltering to isolate from said EMG signals spectral data for a pluralityof frequency bands selected from the range of about 2 Hz to about 1000Hz; calculating a first T-squared value from the spectral data;comparing said first T-squared value to a threshold T-squared value; anddetermining whether to trigger an alarm condition using said comparisonof the first T-squared value to the threshold T-squared value.
 15. Themethod of claim 14 wherein the threshold T-squared value is a value thatis greater than a reference T-squared value.
 16. The method of claim 15further comprising increasing the value of said threshold T-squared if acertain number of false positive events are detected during a givenperiod of time.
 17. The method of claim 15 further comprising providinga patient with an I/O device configured to allow the patient to activatesaid I/O device and identify that an alarm condition was met but noseizure was occurring.
 18. The method of claim 17 further comprisingincreasing the value of said threshold T-squared automatically inresponse to the activation of said I/O device.
 19. The method of claim15 wherein the reference T-squared value is determined from monitoring apatient during a reference period.
 20. The method of claim 19 whereinthe threshold T-squared value is greater than the reference T-squaredvalue by a value that is scaled based on the magnitude of a maximumvoluntary contraction executed during said reference period.
 21. Themethod of claim 14 further comprising: collecting EMG signals while apatient is executing a maximum voluntary contraction and measuring aT-squared value; and setting said threshold T-squared value based on theT-squared value measured while the patient is executing the maximumvoluntary contraction.
 22. The method of claim 21 wherein the thresholdT-squared value is about 100% to about 300% of the T-squared valuemeasured during the maximum voluntary contraction.
 23. The method ofclaim 14 wherein said plurality of frequency bands comprises a firstfrequency range from about 300 Hz to about 400 Hz, a second frequencyrange from about 130 Hz to about 240 Hz, and a third frequency rangefrom about 30 Hz to about 40 Hz.
 24. The method of claim 14 furthercomprising: monitoring a patient during a first reference period;wherein the threshold T-squared value is greater than a referenceT-squared value, the reference T-squared value determined from dataobtained during said first reference period; wherein said firstreference period may be executed in the home environment of saidpatient; and wherein said first reference period is executed during atime period about when the patient first connects the device.
 25. Themethod of claim 24 further comprising: monitoring of the patient duringa second reference period; and replacing said reference T-squared valuewith a second reference T-squared value determined from data obtainedduring the second reference period.
 26. The method of claim 14 whereinthe calculating of said first T-squared value comprises assigningweighting coefficients to different bands among said plurality offrequency bands.
 27. The method of claim 26 wherein the weightingcoefficients adjust the contribution of spectral data from differentbands to said first T-squared value.
 28. The method of claim 27 whereinthe weighting coefficients are factors by which the spectral data ismultiplied prior to normalization of the data using avariance/covariance matrix.
 29. A method of detecting seizurescomprising: monitoring an individual by collecting EMG data during afirst reference period; determining a first reference T-squared valuefrom EMG data obtained during said first reference period; determining aT-squared value while the patient is executing said maximum voluntarycontraction; calculating a difference between said first referenceT-squared value and said T-squared value determined while the patient isexecuting said maximum voluntary contraction; monitoring an individualby collecting EMG data during a second reference period; wherein thesecond reference period is executed in the home environment of theindividual; determining a second reference I-squared value during saidsecond reference period; setting a threshold T-squared value; whereinsaid threshold T-squared value is a value that is greater than thesecond reference T-squared value by a scaling factor related to saiddifference between said first reference I-squared value and saidT-squared value determined while the patient is executing said maximumvoluntary contraction; and monitoring the patient by collecting EMG dataduring a time period following said second reference period and in thehome environment of the patient.
 30. The method of claim 29 wherein saidscaling factor is about 50% to about 500% of said difference.
 31. Themethod of claim 14 further comprising activation of a device that may beused to abort or attenuate a seizure.
 32. An apparatus for detectingseizures, the apparatus comprising: one or more EMG electrodes capableof providing an EMG signal substantially representing seizure-relatedmuscle activity; and a processor configured to receive the EMG signal,process the EMG signal to determine whether a seizure may be occurring,and generate an alert if a seizure is determined to be occurring basedon the EMG signal; wherein the processor is capable of processing theEMG signal to determine whether a seizure may be occurring bycalculating a T-squared statistical value.
 33. The apparatus of claim 32further comprising an environmental transceiver associated with one ormore environmental objects.
 34. The apparatus of claim 33 wherein theenvironmental transceiver is configured to send a signal to a basestation that is dependent upon the relative position of the processorand the environmental transceiver.
 35. The apparatus of claim 34 whereinthe base station comprises a processor capable of processing signalsfrom the environmental transceiver, selecting a template from at leasttwo template files available to the processor, and using the selectedtemplate file in processing the EMG signal.
 36. The apparatus of claim32, wherein the apparatus is configured with GPS technology.